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JPH10149198A - Noise reduction device - Google Patents

Noise reduction device

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Publication number
JPH10149198A
JPH10149198A JP8310324A JP31032496A JPH10149198A JP H10149198 A JPH10149198 A JP H10149198A JP 8310324 A JP8310324 A JP 8310324A JP 31032496 A JP31032496 A JP 31032496A JP H10149198 A JPH10149198 A JP H10149198A
Authority
JP
Japan
Prior art keywords
spectrum
noise
unit
noise reduction
input
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
JP8310324A
Other languages
Japanese (ja)
Other versions
JP4006770B2 (en
Inventor
Toshiyuki Morii
利幸 森井
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Panasonic Holdings Corp
Original Assignee
Matsushita Electric Industrial Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Matsushita Electric Industrial Co Ltd filed Critical Matsushita Electric Industrial Co Ltd
Priority to JP31032496A priority Critical patent/JP4006770B2/en
Priority to CNB97191558XA priority patent/CN1167047C/en
Priority to EP00121445A priority patent/EP1074977B1/en
Priority to EP00126851A priority patent/EP1094447B1/en
Priority to DE69712537T priority patent/DE69712537T2/en
Priority to CNA2005100714801A priority patent/CN1677489A/en
Priority to CA2551458A priority patent/CA2551458C/en
Priority to DE69723324T priority patent/DE69723324T2/en
Priority to KR1020017001046A priority patent/KR100339168B1/en
Priority to CNB031603556A priority patent/CN1262994C/en
Priority to US09/101,186 priority patent/US6453288B1/en
Priority to KR10-2003-7012052A priority patent/KR20040000406A/en
Priority to CN2006101007075A priority patent/CN1877698B/en
Priority to EP00121446A priority patent/EP1071077B1/en
Priority to CNB01132421XA priority patent/CN1170268C/en
Priority to EP99126130A priority patent/EP0992981B1/en
Priority to EP01108523A priority patent/EP1132894B1/en
Priority to CNB200310114349XA priority patent/CN1223994C/en
Priority to CA2614625A priority patent/CA2614625C/en
Priority to DE69712927T priority patent/DE69712927T2/en
Priority to EP00121460A priority patent/EP1071079B1/en
Priority to DE69708693.3T priority patent/DE69708693C5/en
Priority to DE69710794T priority patent/DE69710794T2/en
Priority to DE69712535T priority patent/DE69712535T2/en
Priority to EP00126299A priority patent/EP1136985B1/en
Priority to DE69721595T priority patent/DE69721595T2/en
Priority to CNB011324228A priority patent/CN1188833C/en
Priority to EP00121447A priority patent/EP1071078B1/en
Priority to CA2483280A priority patent/CA2483280C/en
Priority to EP00121458A priority patent/EP1074978B1/en
Priority to CA002356049A priority patent/CA2356049C/en
Priority to AU48842/97A priority patent/AU4884297A/en
Priority to KR1019980705215A priority patent/KR100306817B1/en
Priority to CA002355975A priority patent/CA2355975C/en
Priority to CNB011324201A priority patent/CN1169117C/en
Priority to DE69711715T priority patent/DE69711715T2/en
Priority to CN2006100799202A priority patent/CN1845239B/en
Priority to EP00121466A priority patent/EP1071081B1/en
Priority to CNB011324198A priority patent/CN1170267C/en
Priority to EP99126131A priority patent/EP0992982B1/en
Priority to EP00126875A priority patent/EP1085504B1/en
Priority to KR1020017010774A priority patent/KR20030096444A/en
Priority to DE69712538T priority patent/DE69712538T2/en
Priority to KR1020017001044A priority patent/KR100326777B1/en
Priority to CN2011100659405A priority patent/CN102129862B/en
Priority to CA002355978A priority patent/CA2355978C/en
Priority to CA002242345A priority patent/CA2242345C/en
Priority to CA002355973A priority patent/CA2355973C/en
Priority to DE69708696T priority patent/DE69708696T2/en
Priority to EP02000123A priority patent/EP1217614A1/en
Priority to EP99126132A priority patent/EP0991054B1/en
Priority to HK99102382.4A priority patent/HK1017472B/en
Priority to EP99126129A priority patent/EP0994462B1/en
Priority to EP97911460A priority patent/EP0883107B9/en
Priority to CA002356041A priority patent/CA2356041C/en
Priority to DE69713633T priority patent/DE69713633T2/en
Priority to CA002356051A priority patent/CA2356051C/en
Priority to EP00121464A priority patent/EP1071080B1/en
Priority to DE69712539T priority patent/DE69712539T2/en
Priority to DE69715478T priority patent/DE69715478T2/en
Priority to DE69708697T priority patent/DE69708697T2/en
Priority to CNB011324244A priority patent/CN1170269C/en
Priority to DE69712928T priority patent/DE69712928T2/en
Priority to DE69730316T priority patent/DE69730316T2/en
Priority to EP00121467A priority patent/EP1071082B1/en
Priority to DE69710505T priority patent/DE69710505T2/en
Priority to PCT/JP1997/004033 priority patent/WO1998020483A1/en
Priority to CNB011324236A priority patent/CN1178204C/en
Publication of JPH10149198A publication Critical patent/JPH10149198A/en
Priority to HK02103544.3A priority patent/HK1041969B/en
Priority to HK02103542.5A priority patent/HK1041967B/en
Priority to HK02103543.4A priority patent/HK1041968B/en
Priority to HK02103541.6A priority patent/HK1041966B/en
Priority to HK04107704.8A priority patent/HK1064788B/en
Priority to HK04107703.9A priority patent/HK1064787B/en
Priority to HK07101568.3A priority patent/HK1096761B/en
Priority to HK02103546.1A priority patent/HK1041971B/en
Priority to HK02103545.2A priority patent/HK1041970B/en
Priority to US09/440,087 priority patent/US6330534B1/en
Priority to US09/440,093 priority patent/US6910008B1/en
Priority to US09/440,083 priority patent/US6421639B1/en
Priority to US09/440,199 priority patent/US6345247B1/en
Priority to US09/440,092 priority patent/US6330535B1/en
Priority to KR1020017001045A priority patent/KR100304391B1/en
Priority to KR1020017001040A priority patent/KR100306816B1/en
Priority to KR1020017001039A priority patent/KR100306815B1/en
Priority to KR1020017001038A priority patent/KR100306814B1/en
Priority to US09/843,877 priority patent/US6799160B2/en
Priority to US09/843,938 priority patent/US6772115B2/en
Priority to US09/843,939 priority patent/US6947889B2/en
Priority to US09/849,398 priority patent/US7289952B2/en
Priority to US09/855,708 priority patent/US6757650B2/en
Priority to US10/036,451 priority patent/US20020099540A1/en
Priority to US11/126,171 priority patent/US7587316B2/en
Priority to US11/421,932 priority patent/US7398205B2/en
Priority to US11/508,852 priority patent/US20070100613A1/en
Priority to HK07103753.4A priority patent/HK1097945B/en
Application granted granted Critical
Publication of JP4006770B2 publication Critical patent/JP4006770B2/en
Priority to US12/134,256 priority patent/US7809557B2/en
Priority to US12/198,734 priority patent/US20090012781A1/en
Priority to US12/781,049 priority patent/US8036887B2/en
Priority to US12/870,122 priority patent/US8086450B2/en
Priority to US13/302,677 priority patent/US8370137B2/en
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Abstract

(57)【要約】 【課題】 音声符号化・復号化装置や音声入出力装置を
構成する、入力音声からノイズ成分を除去するノイズ削
減装置において、ノイズスペクトル推定が可能で、異音
感を低減し、音質の劣化を抑えることを目的とする。 【解決手段】 ノイズ推定部２４は、ノイズスペクトル
を推定してそれをノイズスペクトル格納部２５に格納
し、ノイズ削減／スペクトル補償部１８は、ノイズスペ
クトルを入力スペクトルから減じて且つ減じすぎた周波
数のスペクトルを補償し、スペクトル安定化部１９は、
補償後のスペクトルを安定化処理し且つ複素スペクトル
の位相を調整するように構成することにより、音声区間
内でも音声区間外でもノイズスペクトル推定を行うこと
が可能で、音質の劣化を抑えることのできる優れたノイ
ズ削減装置が得られる。 (57) [Summary] [PROBLEMS] A noise reduction device that constitutes a speech encoding / decoding device or a speech input / output device and that removes a noise component from input speech can perform noise spectrum estimation and reduce abnormal noise. It is intended to suppress deterioration of sound quality. A noise estimation unit estimates a noise spectrum and stores the noise spectrum in a noise spectrum storage unit, and a noise reduction / spectrum compensation unit reduces a noise spectrum from an input spectrum and calculates a frequency of an excessively reduced frequency. Compensating the spectrum, the spectrum stabilizing unit 19
By configuring so as to stabilize the compensated spectrum and adjust the phase of the complex spectrum, it is possible to perform noise spectrum estimation both inside and outside the voice section, and to suppress deterioration in sound quality. An excellent noise reduction device is obtained.

Description

【発明の詳細な説明】DETAILED DESCRIPTION OF THE INVENTION

【０００１】[0001]

【発明の属する技術分野】本発明は、ディジタル携帯電
話やマルチメディア通信等に必要な音声符号化・復号化
装置（音声コーデック）や、音声入出力装置を構成する
ために、入力された音声から背景ノイズ成分を除去する
ノイズ削減装置に関するものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a speech encoding / decoding device (speech codec) necessary for digital cellular phones and multimedia communications, and a speech input / output device for constructing a speech input / output device. The present invention relates to a noise reduction device for removing a background noise component.

【０００２】[0002]

【従来の技術】携帯電話等のディジタル移動通信の分野
では、加入者の増加に対処するために低ビットレートの
音声の圧縮符号化法が求められており、各研究機関にお
いて研究開発が進んでいる。日本国内においては、モト
ローラ社の開発したＶＳＥＬＰ（１１．２ｋｂｐｓ）、
ＮＴＴ移動通信網株式会社の開発したＰＳＩ−ＣＥＬＰ
（５．６ｋｂｐｓ）がディジタル携帯電話用の標準符号
化方式として採用され、同方式を搭載したディジタル携
帯電話が国内において既に発売されている。また国際的
には、ＩＴＵ−Ｔの標準化では１６ｋｂｐｓ（ＬＤ−Ｃ
ＥＬＰ）、８ｋｂｐｓ（ＣＳ−ＡＣＥＬＰ）が標準化さ
れ、現在製品開発の段階にある。2. Description of the Related Art In the field of digital mobile communications such as mobile phones, a compression coding method of low bit rate speech is required to cope with an increase in the number of subscribers. I have. In Japan, Motorola's VSELP (11.2 kbps),
PSI-CELP developed by NTT Mobile Communication Network Co., Ltd.
(5.6 kbps) has been adopted as a standard encoding system for digital mobile phones, and digital mobile phones equipped with the same system have already been released in Japan. Internationally, ITU-T standardizes 16 kbps (LD-C
ELP) and 8 kbps (CS-ACELP) have been standardized and are currently in the stage of product development.

【０００３】これらの方式は、いずれもＣＥＬＰ（Ｃｏ
ｄｅ Ｅｘｉｔｅｄ ＬｉｎｅａｒＰｒｅｄｉｃｔｉｏ
ｎ： M.R.Schroeder ”High Quality Speech at Low Bi
tRates”Proc.ICASSP'85 pp.937-940に記載）という方
式を改良したものである。これは、音声を音源情報と声
道情報とに分離し、音源情報については、符号帳に格納
された複数の音源サンプルのインデクスによって符号化
し、声道情報については、ＬＰＣ（線形予測係数）を符
号化するとともに、音源情報符号化の際には、声道情報
を加味して入力音声と比較を行なうという方法（Ａ−ｂ
−Ｓ：Ａｎａｌｙｓｉｓ ｂｙ Ｓｙｎｔｈｅｓｉｓ）
を採用していることに特徴がある。[0003] All of these systems are CELP (Co
de Exited LinearPredictio
n: MRSchroeder "High Quality Speech at Low Bi
tRates ”Proc.ICASSP'85 pp.937-940), which separates speech into sound source information and vocal tract information, and stores the sound source information in a codebook. The vocal tract information is encoded by LPC (Linear Prediction Coefficient), and the vocal tract information is compared with the input voice by taking the vocal tract information into account. (Ab
-S: Analysis by Synthesis)
The feature is that it is adopted.

【０００４】上記技術により、非常に低ビットレートで
音声信号を伝送することができるようになったが、それ
と共に大きな問題点が明らかになった。それは、音声の
発声モデルに基づいて情報圧縮を行っているために、音
声信号以外の音響信号に対応できないという点である。
そのため、音声信号中に背景ノイズや機器ノイズが含ま
れていると、効率の良い符号化が出来ず、合成時（復号
化時）に異音を生じる結果となっていた。[0004] Although the above technique has enabled the transmission of audio signals at a very low bit rate, it has also revealed a major problem. That is, since information compression is performed based on a voice utterance model, it cannot respond to acoustic signals other than voice signals.
Therefore, if background noise or device noise is included in the audio signal, efficient encoding cannot be performed, and abnormal noise is generated at the time of synthesis (at the time of decoding).

【０００５】この問題を解決するために、従来より入力
音声信号からノイズを削減する手法が検討されてきた。
上記標準化方式のＰＳＩ−ＣＥＬＰでは、符号化を行う
前にノイズキヤンセラによってノイズを削減するという
処理を行っている。上記ノイズキャンセラは、カルマン
フィルタを基本として開発されており、音声の有無を検
出して、適応的に制御を行うことによりノイズを低減さ
せている。このノイズキャンセラによって、ある程度の
背景ノイズを削減することができる。しかし、ノイズレ
ベルの高いノイズや、音声中のノイズ等に対しては余り
良い性能が得られていなかった。[0005] In order to solve this problem, techniques for reducing noise from an input voice signal have been studied.
In the standardized PSI-CELP, a process of reducing noise by a noise canceller is performed before encoding. The noise canceller has been developed based on a Kalman filter, and reduces noise by detecting the presence or absence of speech and performing adaptive control. This noise canceller can reduce some background noise. However, very good performance has not been obtained with respect to noise having a high noise level, noise in voice, and the like.

【０００６】一方、より強力なノイズ低減法として、ス
ペクトルサブトラクション法が挙げられる。（S.F.Boll
"Suppression of Acoustic Noise in Speech Using Sp
ectral Subtraction", IEEE Trans.ASSP.,Vol.27,No.2,
pp113-120,1979に記載）。これは、入力音声信号に対し
て離散フーリエ変換を行ってスペクトルに変換した後、
ノイズをスペクトル上で減ずる方法であり、主に音声認
識装置の入力部等に応用されている。On the other hand, as a more powerful noise reduction method, there is a spectrum subtraction method. (SFBoll
"Suppression of Acoustic Noise in Speech Using Sp
ectral Subtraction ", IEEE Trans.ASSP., Vol.27, No.2,
pp 113-120, 1979). This is done by performing a discrete Fourier transform on the input audio signal and converting it to a spectrum,
This is a method of reducing noise on a spectrum, and is mainly applied to an input unit or the like of a speech recognition device.

【０００７】この方法を音声信号中のノイズ低減に応用
した一例について、図２を用いて説明する。すなわち、
ノイズスペクトルの推定は次の手順で行われる。まず、
音声を含んでいないノイズのみの信号３１を入力し、Ａ
／Ｄ変換部３２においてディジタル信号に変換する。次
に、フーリエ変換部３３において、一定時間長の入力信
号列（フレームと呼ぶ）に対して離散フーリエ変換を行
い、ノイズのスペクトルを求める。そして、ノイズ分析
部３４において、複数のフレームに対して求めたノイズ
のスペクトルからノイズの平均的スペクトルを求め、こ
れをノイズスペクトル格納部３５に格納する。そして、
ノイズの削減は以下の手順で行われる。An example in which this method is applied to noise reduction in an audio signal will be described with reference to FIG. That is,
The noise spectrum is estimated in the following procedure. First,
A signal 31 containing only noise without voice is input, and A
The signal is converted into a digital signal by the / D converter 32. Next, the Fourier transform unit 33 performs a discrete Fourier transform on an input signal sequence (referred to as a frame) having a fixed time length to obtain a noise spectrum. Then, the noise analysis unit 34 calculates an average spectrum of the noise from the noise spectra obtained for the plurality of frames, and stores the average spectrum in the noise spectrum storage unit 35. And
Noise reduction is performed in the following procedure.

【０００８】ノイズを含む音声信号３６を入力し、Ａ／
Ｄ変換部３７においてディジタル信号に変換する。次
に、上記と同様にしてフーリエ変換部３８で離散フーリ
エ変換を行い、ノイズを含んだ音声のスペクトルを求め
る。そして、ノイズ削減部３９において、ノイズスペク
トル格納部３５に格納されたノイズスペクトルを、音声
のスペクトルから減ずる。その結果得られたスペクトル
に対して、逆フーリエ変換部４０において逆フーリエ変
換を行い、出力信号４１を得る。An audio signal 36 containing noise is input, and A /
The signal is converted into a digital signal by the D conversion unit 37. Next, the discrete Fourier transform is performed by the Fourier transform unit 38 in the same manner as described above, and the spectrum of the voice including noise is obtained. Then, the noise reduction unit 39 subtracts the noise spectrum stored in the noise spectrum storage unit 35 from the voice spectrum. The spectrum obtained as a result is subjected to an inverse Fourier transform in an inverse Fourier transform unit 40 to obtain an output signal 41.

【０００９】なお、このアルゴリズムにおけるスペクト
ルとしては、振幅スペクトル（複素数の複素平面上での
ノルム、実数部と虚数部を２乗して加算し、平方根をと
ることによって求められる。）を用いるのが一般的であ
る。また、振幅スペクトルで減じた場合、逆フーリエ変
換を行う時の位相成分としては、入力信号の位相成分を
そのまま用いるという方法が挙げられる。As the spectrum in this algorithm, an amplitude spectrum (a norm of a complex number on a complex plane, which is obtained by squaring and adding a real part and an imaginary part and taking a square root) is used. General. In addition, when the amplitude component is reduced by the amplitude spectrum, there is a method of directly using the phase component of the input signal as the phase component when performing the inverse Fourier transform.

【００１０】[0010]

【発明が解決しようとする課題】上記スペクトルサブト
ラクション法は、より強力なノイズ低減方法であるが、
ノイズ推定が困難であるために、これまでのリアルタイ
ムの音声処理装置に用いられた例は少なかった。The above-described spectral subtraction method is a more powerful noise reduction method.
Due to the difficulty in estimating noise, there have been few examples used in real-time speech processing devices up to now.

【００１１】すなわち、上記スペクトルサブトラクショ
ン法をリアルタイムの音声処理装置に応用するために
は、次の様な課題を有していた。 ＜１＞音声がどのタイミングでデータ中に存在するかが
明らかでないために、ノイズスペクトル推定が困難。 ＜２＞ノイズレベルが高い時にスペクトルが大きく歪
み、音質の劣化を生ずる。 ＜３＞無音区間（音声の無いノイズのみの区間）におい
て異音感を生ずる。That is, in order to apply the above-described spectral subtraction method to a real-time speech processing apparatus, there are the following problems. <1> It is difficult to estimate the noise spectrum because it is not clear at what timing the voice exists in the data. <2> When the noise level is high, the spectrum is greatly distorted, and the sound quality is deteriorated. <3> Abnormal sound is generated in a silent section (a section including only noise without sound).

【００１２】本発明は、ノイズスペクトル推定が可能
で、異音感を低減し、音質の劣化を抑えるノイズ削減装
置の提供を目的とする。It is an object of the present invention to provide a noise reduction apparatus capable of estimating a noise spectrum, reducing abnormal noise, and suppressing deterioration of sound quality.

【００１３】[0013]

【課題を解決するための手段】上記課題を解決するため
に本発明は、ノイズ推定部が、フーリエ変換部により得
られる入力スペクトルとノイズスペクトル格納部に格納
されているノイズスペクトルとを比較することによって
ノイズのスペクトルを推定し、得られたノイズスペクト
ルをノイズスペクトル格納部に格納し、ノイズ削減／ス
ペクトル補償部が、ノイズ削減係数調節部により得られ
る係数に基づいてノイズスペクトル格納部に格納されて
いるノイズスペクトルを、フーリエ変換部により得られ
る入力スペクトルから減じ、得られたスペクトルを調べ
て減じすぎた周波数のスペクトルを補償し、スペクトル
安定化部が、ノイズ削減／スペクトル補償部により得ら
れたスペクトルを安定化処理するとともに、フーリエ変
換部により得られた複素スペクトルの位相のうちノイズ
削減／スペクトル補償部において補償された周波数の位
相を調整するように構成したものである。According to the present invention, a noise estimating unit compares an input spectrum obtained by a Fourier transform unit with a noise spectrum stored in a noise spectrum storing unit. The noise spectrum is estimated by the noise spectrum storage unit, the obtained noise spectrum is stored in the noise spectrum storage unit, and the noise reduction / spectrum compensation unit is stored in the noise spectrum storage unit based on the coefficient obtained by the noise reduction coefficient adjustment unit. The noise spectrum is subtracted from the input spectrum obtained by the Fourier transform unit, and the obtained spectrum is checked to compensate for the spectrum of the frequency that has been excessively reduced. Is stabilized by the Fourier transform unit. It is obtained by configured to adjust the phase of the compensated frequency in the noise reduction / spectrum compensating section of the phase of the complex spectrum.

【００１４】これにより、音声区間内でも音声区間外で
もノイズスペクトル推定を行うことが可能で、音質の劣
化を抑えることのできる優れたノイズ削減装置が得られ
る。Thus, it is possible to perform noise spectrum estimation both inside and outside the voice section, and to obtain an excellent noise reduction apparatus capable of suppressing deterioration of sound quality.

【００１５】[0015]

【発明の実施の形態】本発明の請求項１に記載の発明
は、入力音声信号をディジタル信号に変換するＡ／Ｄ変
換部と、削減量を決定する係数を調節するノイズ削減係
数調節部と、前記Ａ／Ｄ変換部により得られる一定時間
長（１フレーム）のディジタル信号に対して線形予測分
析（ＬＰＣ分析）を行うＬＰＣ分析部と、前記Ａ／Ｄ変
換部により得られる一定時間長のディジタル信号に対し
て離散フーリエ変換を行い入力スペクトルと複素スペク
トルを得るフーリエ変換部と、推定されたノイズのスペ
クトルを格納するノイズスペクトル格納部と、前記フー
リエ変換部により得られる入力スペクトルと前記ノイズ
スペクトル格納部に格納されているノイズスペクトルと
を比較することによってノイズのスペクトルを推定し、
得られたノイズスペクトルを前記ノイズスペクトル格納
部に格納するノイズ推定部と、前記ノイズ削減係数調節
部により得られる係数に基づいて前記ノイズスペクトル
格納部に格納されているノイズスペクトルを前記フーリ
エ変換部により得られる入力スペクトルから減じ、更
に、得られるスペクトルを調べ、減じすぎた周波数のス
ペクトルを補償するノイズ削減／スペクトル補償部と、
前記ノイズ削減／スペクトル補償部により得られたスペ
クトルを安定化処理するとともに、前記フーリエ変換部
により得られた複素スペクトルの位相のうち前記ノイズ
削減／スペクトル補償部において補償された周波数の位
相を調整するスペクトル安定化部と、前記スペクトル安
定化部において安定化処理されたスペクトルと調整され
た位相スペクトルとに基づいて逆フーリエ変換を行う逆
フーリエ変換部と、前記逆フーリエ変換部により得られ
た信号に対してスペクトル強調を行うスペクトル強調部
と、前記スペクトル強調部により得られた信号を前のフ
レームの信号と整合させる波形整合部とを備えることを
特徴としたノイズ削減装置であり、音声区間中でも音声
区間外でもノイズスペクトル推定を行うことができると
ともに、入力のスペクトル包絡の特徴を線形予測係数で
強調することができるという作用を有する。DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The invention according to claim 1 of the present invention comprises an A / D converter for converting an input audio signal into a digital signal, and a noise reduction coefficient adjuster for adjusting a coefficient for determining a reduction amount. An LPC analysis unit that performs a linear prediction analysis (LPC analysis) on a digital signal of a fixed time length (one frame) obtained by the A / D conversion unit, and a fixed time length obtained by the A / D conversion unit. A Fourier transform unit for performing a discrete Fourier transform on the digital signal to obtain an input spectrum and a complex spectrum, a noise spectrum storage unit for storing an estimated noise spectrum, an input spectrum obtained by the Fourier transform unit, and the noise spectrum Estimate the noise spectrum by comparing the noise spectrum stored in the storage unit,
A noise estimation unit that stores the obtained noise spectrum in the noise spectrum storage unit, and a noise spectrum that is stored in the noise spectrum storage unit based on the coefficient obtained by the noise reduction coefficient adjustment unit. A noise reduction / spectrum compensator for subtracting from the obtained input spectrum, further examining the obtained spectrum, and compensating for the spectrum of the excessively reduced frequency;
Stabilizing the spectrum obtained by the noise reduction / spectrum compensator, and adjusting the phase of the frequency compensated by the noise reducer / spectrum compensator among the phases of the complex spectrum obtained by the Fourier transformer. A spectrum stabilizing unit, an inverse Fourier transform unit that performs an inverse Fourier transform based on the spectrum that has been subjected to the stabilization process and the adjusted phase spectrum in the spectrum stabilizing unit, and a signal obtained by the inverse Fourier transform unit. A noise reduction apparatus comprising: a spectrum emphasizing unit that performs spectrum emphasis on the signal; and a waveform matching unit that matches a signal obtained by the spectrum emphasizing unit with a signal of a previous frame. Noise spectrum estimation can be performed outside the interval, and the input The characteristics of the vector envelope has an effect that it is possible to emphasize the linear prediction coefficients.

【００１６】請求項２に記載の発明は、予めノイズ区間
であるかどうかの判定を行ない、ノイズであると判定し
た場合には、フーリエ変換部により得られる入力スペク
トルを各周波数毎に補償用ノイズスペクトルと大小比較
し、補償用ノイズスペクトルより小さい場合にその周波
数の補償用ノイズスペクトルを入力スペクトルとするこ
とによって補償用ノイズスペクトルを推定し、またそれ
とは別に、入力スペクトルを一定割合で加算していくこ
とによって平均ノイズスペクトルを推定し、さらに、補
償用ノイズスペクトルと平均ノイズスペクトルとをノイ
ズスペクトル格納部に格納するノイズ推定部を備えるこ
とを特徴とする請求項１記載のノイズ削減装置であり、
ノイズのスペクトルを平均と最低の２つの方向から推定
することにより、より的確な削減処理を行うことができ
るという作用を有する。According to a second aspect of the present invention, it is determined in advance whether or not the noise section is a noise section. If it is determined that the noise section is a noise section, the input spectrum obtained by the Fourier transform unit is subjected to noise compensation for each frequency. The spectrum is compared with the spectrum, and if it is smaller than the compensation noise spectrum, the compensation noise spectrum at that frequency is used as the input spectrum to estimate the compensation noise spectrum. The noise reduction device according to claim 1, further comprising: a noise estimating unit that estimates an average noise spectrum by performing the calculation, and further stores a noise spectrum for compensation and an average noise spectrum in a noise spectrum storage unit.
By estimating the noise spectrum from the average and the lowest two directions, it is possible to perform more accurate reduction processing.

【００１７】請求項３に記載の発明は、ノイズ削減係数
調節部にて得られたノイズ削減係数をノイズスペクトル
格納部に格納された平均ノイズスペクトルに乗じて、フ
ーリエ変換部にて得られた入力スペクトルから減じ、負
のスペクトル値になってしまった周波数に対してはノイ
ズスペクトル格納部に格納された補償用ノイズスペクト
ルにより補償するノイズ削減／スペクトル補償部を備え
ることを特徴とする請求項２記載のノイズ削減装置であ
り、ノイズの平均スペクトルを削減に用いることによ
り、より大きくノイズスペクトルを削減することができ
るとともに、補償用スペクトルを別に推定したことによ
り、より的確な補償を行うことができるという作用を有
する。According to a third aspect of the present invention, the noise reduction coefficient obtained by the noise reduction coefficient adjustment unit is multiplied by the average noise spectrum stored in the noise spectrum storage unit, and the input obtained by the Fourier transform unit is obtained. 3. The apparatus according to claim 2, further comprising a noise reduction / spectrum compensator for compensating for a frequency which is subtracted from the spectrum and becomes a negative spectrum value by using a compensation noise spectrum stored in the noise spectrum storage. It is said that the noise reduction device can reduce the noise spectrum more greatly by using the average spectrum of the noise, and can perform more accurate compensation by separately estimating the compensation spectrum. Has an action.

【００１８】請求項４に記載の発明は、ノイズ削減／ス
ペクトル補償部にてノイズ削減とスペクトル補償をなさ
れたスペクトルの全域パワーと聴感的に重要な一部の帯
域のパワー（中域パワー）とを調べ、入力された信号が
無音区間（音声のないノイズのみの信号）かどうかを識
別し、無音区間と判断した場合には、全域パワーと中域
パワーに対して安定化処理とパワー低減処理を行なうス
ペクトル安定化部を備えることを特徴とする請求項１記
載のノイズ削減装置であり、音声の含まれていないノイ
ズのみの区間のスペクトルをスムージングすることがで
きるとともに、同区間のスペクトルがノイズ削減のため
に極端なスペクトル変動を起こすことを防ぐという作用
を有する。According to a fourth aspect of the present invention, the power of the entire band of the spectrum subjected to noise reduction and spectrum compensation by the noise reduction / spectrum compensation unit and the power of a part of the band that is audibly important (middle band power) are calculated. To determine whether the input signal is a silent section (a signal containing only noise without sound), and if it is determined to be a silent section, perform stabilization processing and power reduction processing for the entire band power and the middle band power. 2. The noise reduction apparatus according to claim 1, further comprising: a spectrum stabilizing unit that performs a smoothing process on a spectrum of a section including only noise without voice, and a spectrum of the section including the noise. It has the effect of preventing extreme spectral fluctuations for reduction.

【００１９】請求項５に記載の発明は、フーリエ変換部
で得られた複素スペクトルに対して、ノイズ削減／スペ
クトル補償部でスペクトル補償を受けたかどうかの情報
を基に、乱数による位相回転を行なうスペクトル安定化
部を備えることを特徴とする請求項１記載のノイズ削減
装置であり、補償された周波数成分の位相にランダム性
を持たせ、削減できずに残ったノイズを、聴感的に異音
感の少ないノイズに変換させることができるという作用
を有する。According to a fifth aspect of the present invention, a phase rotation based on random numbers is performed on the complex spectrum obtained by the Fourier transform unit, based on information on whether or not the spectrum has been compensated by the noise reduction / spectral compensation unit. 2. The noise reduction apparatus according to claim 1, further comprising a spectrum stabilizing unit, wherein the phase of the compensated frequency component has randomness, and the noise that cannot be reduced is audibly disturbed. This has the effect that the noise can be converted into noise with less noise.

【００２０】請求項６に記載の発明は、予めスペクトル
強調に用いる重み係数のセットを複数用意し、ノイズ削
減時には、入力された信号の状態に応じて重み付け係数
のセットを選択し、選択された重み付け係数を用いてス
ペクトル強調を行なうスペクトル強調部を備えることを
特徴とする請求項１記載のノイズ削減装置であり、音声
区間においては、聴感的により適当な重み付けができ、
無音区間や無声子音区間においては、聴感重み付けによ
る異音感を押さえることができるという作用を有する。According to the sixth aspect of the present invention, a plurality of sets of weighting coefficients used for spectrum enhancement are prepared in advance, and at the time of noise reduction, a set of weighting coefficients is selected according to the state of an input signal, and the selected set is selected. The noise reduction device according to claim 1, further comprising a spectrum emphasis unit that performs spectrum emphasis using a weighting coefficient, and in a voice section, more appropriate perceptual weighting can be performed.
In a silent section or an unvoiced consonant section, an effect of suppressing abnormal sound due to hearing weighting is provided.

【００２１】以下、本発明の実施の形態について、図１
を用いて説明する。 （実施の形態）図１は、本実施の形態におけるノイズ削
減装置の主要部の機能ブロック図である。図１におい
て、１１は入力信号、１２はＡ／Ｄ変換部、１３はノイ
ズ削減係数格納部、１４はノイズ削減係数調整部、１５
は入力波形設定部、１６は入力波形設定部、１７はフー
リエ変換部、１８はノイズ削減／スペクトル補償部、１
９はスペクトル安定部、２０は逆フーリエ変換部、２１
はスペクトル強調部、２２は波形整合部、２３は出力信
号、２４はノイズ推定部、２５はノイズスペクトル格納
部、２６は前スペクトル格納部、２７は乱数位相格納
部、２８は前波形格納部、２９は最大パワー格納部であ
る。Hereinafter, an embodiment of the present invention will be described with reference to FIG.
This will be described with reference to FIG. (Embodiment) FIG. 1 is a functional block diagram of a main part of a noise reduction device according to the present embodiment. In FIG. 1, 11 is an input signal, 12 is an A / D converter, 13 is a noise reduction coefficient storage, 14 is a noise reduction coefficient adjuster, 15
Is an input waveform setting unit, 16 is an input waveform setting unit, 17 is a Fourier transform unit, 18 is a noise reduction / spectrum compensation unit,
9 is a spectrum stabilizing section, 20 is an inverse Fourier transform section, 21
Is a spectrum emphasis unit, 22 is a waveform matching unit, 23 is an output signal, 24 is a noise estimation unit, 25 is a noise spectrum storage unit, 26 is a previous spectrum storage unit, 27 is a random number phase storage unit, 28 is a previous waveform storage unit, 29 is a maximum power storage unit.

【００２２】始めに初期設定について説明する。（表
１）に、固定パラメータの名称と設定例を示す。First, the initial setting will be described. (Table 1) shows fixed parameter names and setting examples.

【００２３】[0023]

【表１】 [Table 1]

【００２４】また、乱数位相格納部２７には、位相を調
整するための位相データを格納しておく。これらは、ス
ペクトル安定化部１９において、位相を回転させるため
に用いられる。位相データが８種類の場合の例を（表
２）に示す。The random number phase storage 27 stores phase data for adjusting the phase. These are used in the spectrum stabilizing unit 19 to rotate the phase. (Table 2) shows an example in which eight types of phase data are used.

【００２５】[0025]

【表２】 [Table 2]

【００２６】さらに、上記位相データを用いるためのカ
ウンター（乱数位相カウンター）も、乱数位相格納部２
７に格納しておく。この値は、予め０に初期化して格納
しておく。Further, a counter (random number phase counter) for using the above-mentioned phase data is also provided in the random number phase storage unit 2.
7 is stored. This value is initialized to 0 in advance and stored.

【００２７】次に、スタティックのＲＡＭ領域を設定す
る。すなわち、ノイズ削減係数格納部１３、ノイズスペ
クトル格納部２５、前スペクトル格納部２６、前波形格
納部２８、最大パワー格納部２９をクリアする。以下
に、各格納部の説明と設定例を述べる。Next, a static RAM area is set. That is, the noise reduction coefficient storage unit 13, the noise spectrum storage unit 25, the previous spectrum storage unit 26, the previous waveform storage unit 28, and the maximum power storage unit 29 are cleared. Hereinafter, description of each storage unit and a setting example will be described.

【００２８】ノイズ削減係数格納部１３は、ノイズ削減
係数を格納するエリアであり、初期値として２０．０を
格納しておく。ノイズスペクトル格納部２５は、平均ノ
イズパワーと、平均ノイズスペクトルと、１位候補の補
償用ノイズスペクトルと２位候補の補償用ノイズスペク
トルとそれぞれの周波数のスペクトル値が、何フレーム
前に変化したかを示すフレーム数（持続数）を、各周波
数毎に格納するエリアであり、平均ノイズパワーに十分
大きな値、平均ノイズスペクトルに指定最小パワー、補
償用ノイズスペクトルと持続数としてそれぞれに充分大
きな数を初期値として格納しておく。The noise reduction coefficient storage unit 13 is an area for storing a noise reduction coefficient, and stores 20.0 as an initial value. The noise spectrum storage unit 25 stores the average noise power, the average noise spectrum, the number of frames before which the spectrum value of each frequency of the first candidate noise spectrum for compensation and the second candidate noise spectrum for compensation changed. Is an area for storing the number of frames (the number of durations) indicating for each frequency, a sufficiently large value for the average noise power, a specified minimum power for the average noise spectrum, and a sufficiently large number for the noise spectrum for compensation and the number of durations, respectively. It is stored as an initial value.

【００２９】前スペクトル格納部２６は、補償用ノイズ
パワー、以前のフレームのパワー（全域、中域）（前フ
レームパワー）、以前のフレームの平滑化パワー（全
域、中域）（前フレーム平滑化パワー）、及びノイズ連
続数を格納するエリアであり、補償用ノイズパワーとし
て十分大きな値を、前フレームパワー、全フレーム平滑
化パワーとしていずれも０．０を、またノイズ連続数と
してノイズ基準連続数を格納しておく。The pre-spectrum storage section 26 includes compensation noise power, power of the previous frame (entire area, middle area) (previous frame power), and smoothed power of the previous frame (entire area, middle area) (previous frame smoothing). Power) and the number of continuous noises. A sufficiently large value as the noise power for compensation, 0.0 as the previous frame power and the smoothed power of all frames, and the noise reference continuous number as the number of continuous noises. Is stored.

【００３０】前波形格納部２８は、出力信号を整合させ
るための、前のフレームの出力信号の、最後の先読みデ
ータ長分のデータを格納するエリアであり、初期値とし
て全てに０を格納しておく。スペクトル強調部２１は、
ＡＲＭＡ及び高域強調フィルタリングを行なうが、その
ためのそれぞれのフィルターの状態をいずれも０にクリ
アしておく。最大パワー格納部２９は、入力された信号
のパワーの最大を格納するエリアであり、最大パワーと
して０を格納しておく。The pre-waveform storage unit 28 is an area for storing data for the last pre-read data length of the output signal of the previous frame for matching the output signal, and stores 0 as an initial value for all. Keep it. The spectrum enhancement unit 21
ARMA and high-frequency emphasis filtering are performed, and the state of each filter for that purpose is cleared to zero. The maximum power storage unit 29 is an area for storing the maximum power of the input signal, and stores 0 as the maximum power.

【００３１】次にノイズ削減アルゴリズムについて、図
１を用いてブロック毎に説明する。まず、音声を含むア
ナログ入力信号１１をＡ／Ｄ変換部１２でＡ／Ｄ変換
し、１フレーム長＋先読みデータ長（上記設定例では、
１６０＋８０＝２４０ポイント）の分だけ入力する。ノ
イズ削減係数調節部１４は、ノイズ削減係数格納部１３
に格納されたノイズ削減係数と指定ノイズ削減係数とノ
イズ削減係数学習係数と補償パワー上昇係数とを基に、
（数１）により、ノイズ削減係数並びに補償係数を算出
する。そして、得られたノイズ削減係数をノイズ削減係
数格納部１３に格納するとともに、Ａ／Ｄ変換部１２で
得られた入力信号を、入力波形設定部１５へ送り、さら
に補償係数とノイズ削減係数を、ノイズ推定部２４とノ
イズ削減／スペクトル補償部18へ送る。Next, the noise reduction algorithm will be described for each block with reference to FIG. First, the analog input signal 11 including voice is A / D-converted by the A / D converter 12, and one frame length + pre-read data length (in the above setting example,
160 + 80 = 240 points). The noise reduction coefficient adjustment unit 14 includes the noise reduction coefficient storage unit 13
Based on the noise reduction coefficient, the specified noise reduction coefficient, the noise reduction coefficient learning coefficient, and the compensation power increase coefficient stored in
The noise reduction coefficient and the compensation coefficient are calculated by (Equation 1). Then, the obtained noise reduction coefficient is stored in the noise reduction coefficient storage unit 13, and the input signal obtained by the A / D conversion unit 12 is sent to the input waveform setting unit 15, and the compensation coefficient and the noise reduction coefficient are further stored. , To the noise estimation unit 24 and the noise reduction / spectrum compensation unit 18.

【００３２】[0032]

【数１】 (Equation 1)

【００３３】なお、ノイズ削減係数はノイズを減ずる割
合を示した係数、指定ノイズ削減係数は予め指定された
固定削減係数、ノイズ削減係数学習係数はノイズ削減係
数の指定ノイズ削減係数に近づける割合を示した係数、
補償係数はスペクトル補償における補償パワーを調節す
る係数、補償パワー上昇係数は補償係数を調節する係数
である。The noise reduction coefficient is a coefficient indicating the rate of noise reduction, the designated noise reduction coefficient is a fixed reduction coefficient specified in advance, and the noise reduction coefficient learning coefficient is a rate at which the noise reduction coefficient approaches the specified noise reduction coefficient. Coefficient,
The compensation coefficient is a coefficient for adjusting the compensation power in the spectrum compensation, and the compensation power increase coefficient is a coefficient for adjusting the compensation coefficient.

【００３４】入力波形設定部１５においては、Ａ／Ｄ変
換部１２からの入力信号を、ＦＦＴ（高速フーリエ変
換）することができるように、２の指数乗の長さを持つ
メモリ配列に、後ろ詰めで書込む。前の部分は０を詰め
ておく。前述の設定例では、２５６の長さの配列に０〜
１５まで０を書込み、１６〜２５５まで入力信号を書込
む。この配列は、８次のＦＦＴの際に実数部として用い
られる。また、虚数部として、実数部と同じ長さの配列
を用意し、全てに０を書込んでおく。In the input waveform setting section 15, the input signal from the A / D conversion section 12 is rearranged into a memory array having a length of an exponent of 2 so that FFT (Fast Fourier Transform) can be performed. Write with justification. The leading part is padded with zeros. In the above setting example, 0-
Write 0 to 15 and write input signals from 16 to 255. This array is used as a real part in the eighth-order FFT. Also, an array having the same length as the real part is prepared as the imaginary part, and 0 is written in all of them.

【００３５】ＬＰＣ分析部１６においては、入力波形設
定部１５で設定した実数部エリアに対してハミング窓を
掛け、窓掛け後の波形に対して自己相関分析を行って自
己相関係数を求め、自己相関法に基づくＬＰＣ分析を行
い、線形予測係数を得る。さらに、得られた線形予測係
数をスペクトル強調部２１に送る。The LPC analysis section 16 multiplies the real part area set by the input waveform setting section 15 with a Hamming window and performs autocorrelation analysis on the windowed waveform to obtain an autocorrelation coefficient. LPC analysis based on the autocorrelation method is performed to obtain a linear prediction coefficient. Further, the obtained linear prediction coefficient is sent to the spectrum emphasizing unit 21.

【００３６】フーリエ変換部１７は、入力波形設定部１
５で得られる実数部、虚数部のメモリ配列を用いて、Ｆ
ＦＴによる離散フーリエ変換を行う。得られた複素スペ
クトルの実数部と虚数部の絶対値の和を計算することに
よって、入力信号の疑似振幅スペクトル（以下、入力ス
ペクトル）を求める。また、各周波数の入力スペクトル
値の総和（以下、入力パワー）を求め、ノイズ推定部２
４へ送る。また、複素スペクトルそのものを、スペクト
ル安定部１９へ送る。The Fourier transform unit 17 includes the input waveform setting unit 1
Using the memory array of the real part and the imaginary part obtained in
Perform discrete Fourier transform by FT. By calculating the sum of the absolute values of the real part and the imaginary part of the obtained complex spectrum, a pseudo amplitude spectrum (hereinafter, input spectrum) of the input signal is obtained. Further, the sum of the input spectrum values of each frequency (hereinafter, input power) is obtained, and the noise estimation unit 2
Send to 4. Further, the complex spectrum itself is sent to the spectrum stabilizer 19.

【００３７】次に、ノイズ推定部２４における処理を説
明する。ノイズ推定部２４は、フーリエ変換部１７で得
られた入力パワーと最大パワー格納部２９に格納された
最大パワーの値とを比較し、最大パワーの方が小さい場
合は、最大パワー値を入力パワー値として、その値を最
大パワー格納部２９に格納する。そして、以下の３つう
ち少なくとも一つに該当する場合はノイズ推定を行い、
全て満たさない場合はノイズ推定は行わない。 （１）入力パワーが、最大パワーに無音検出係数を乗じ
た値よりも小さい。 （２）ノイズ削減係数が、指定ノイズ削減係数に０．２
を加えたものより大きい。 （３）入力パワーが、ノイズスペクトル格納部２５から
得られる平均ノイズパワーに１．６を乗じたものより小
さい。Next, the processing in the noise estimation unit 24 will be described. The noise estimating unit 24 compares the input power obtained by the Fourier transform unit 17 with the value of the maximum power stored in the maximum power storage unit 29. If the maximum power is smaller, the noise estimating unit 24 calculates the maximum power value as the input power. The value is stored in the maximum power storage unit 29 as a value. When at least one of the following three conditions is satisfied, noise estimation is performed,
If all are not satisfied, no noise estimation is performed. (1) The input power is smaller than a value obtained by multiplying the maximum power by a silence detection coefficient. (2) The noise reduction coefficient is 0.2
Greater than the sum of (3) The input power is smaller than the average noise power obtained from the noise spectrum storage unit 25 multiplied by 1.6.

【００３８】ここで、ノイズ推定部２４におけるノイズ
推定アルゴリズムを述べる。まず、ノイズスペクトル格
納部２５に格納されている１位候補、２位候補の全ての
周波数の持続数を更新する（１を加算する）。そして、
１位候補の各周波数の持続数を調べ、予め設定したノイ
ズスペクトル基準持続数より大きい場合は、２位候補の
補償用スペクトルと持続数を１位候補とし、２位候補の
補償用スペクトルを３位候補の補償用スペクトルとし持
続数を０とする。ただし、この２位候補の補償用スペク
トルの入れ替えにおいては、３位候補を格納せず、２位
候補を若干大きくしたもので代用することによって、メ
モリを節約することができる。本実施の形態では、２位
候補の補償用スペクトルを１．４倍したものを代用する
こととする。Here, a noise estimation algorithm in the noise estimation unit 24 will be described. First, the number of sustained frequencies of all the first and second candidates stored in the noise spectrum storage unit 25 is updated (1 is added). And
The number of durations of each frequency of the first candidate is checked, and if the number is longer than the preset noise spectrum reference number, the compensation spectrum and the number of durations of the second candidate are set to the first candidate, and the compensation spectrum of the second candidate is set to three. The spectrum is used as the compensation spectrum of the position candidate, and the number of durations is set to 0. However, in replacing the compensation spectrum of the second candidate, the memory can be saved by not storing the third candidate but substituting a slightly larger second candidate. In the present embodiment, a value obtained by multiplying the compensation spectrum of the second candidate by 1.4 is used.

【００３９】持続数の更新の後に、各周波数毎に、補償
用ノイズスペクトルと入力スペクトルとの比較を行う。
まず、各周波数の入力スペクトルを１位候補の補償用ノ
イズスペクトルと比較し、もし入力スペクトルの方が小
さい場合は、１位候補の補償用ノイズスペクトルと持続
数を２位候補とし、入力スペクトルを１位候補の補償用
スペクトルとし１位候補の持続数は０とする。前記の条
件以外の場合は、入力スペクトルと２位候補の補償用ノ
イズスペクトルとの比較を行い、もし入力スペクトルの
方が小さい場合は、入力スペクトルを２位候補の補償用
スペクトルとし２位候補の持続数は０とする。そして、
得られた１、２位候補の補償用スペクトルと持続数を補
償用ノイズスペクトル格納部２５に格納する。また、同
時に、平均ノイズスペクトルも、次の（数２）にしたが
って更新する。After the update of the sustain number, a comparison is made between the noise spectrum for compensation and the input spectrum for each frequency.
First, the input spectrum of each frequency is compared with the noise spectrum for compensation of the first candidate. If the input spectrum is smaller, the noise spectrum for compensation of the first candidate and the sustained number are set to the second candidate, and the input spectrum is set to the second candidate. It is assumed that the number of sustained first-place candidates is 0 as the compensation spectrum of the first-place candidate. If the above conditions are not satisfied, the input spectrum is compared with the noise spectrum for compensating the second candidate. If the input spectrum is smaller, the input spectrum is set as the compensating spectrum for the second candidate, and The number of durations is 0. And
The obtained first and second candidate compensation spectra and the sustained numbers are stored in the compensation noise spectrum storage unit 25. At the same time, the average noise spectrum is updated according to the following (Equation 2).

【００４０】[0040]

【数２】 (Equation 2)

【００４１】なお、平均ノイズスペクトルは、疑似的に
求めた平均のノイズスペクトルであり、（数２）におけ
る係数ｇは、平均ノイズスペクトルの学習の早さを調節
する係数である。すなわち、入力パワーがノイズパワー
と比較して小さい場合は、ノイズのみの区間である可能
性が高いとして学習速度を上げ、そうでない場合は、音
声区間中である可能性があるとして学習速度を下げる効
果を持つ係数である。The average noise spectrum is a pseudo average noise spectrum, and the coefficient g in (Equation 2) is a coefficient for adjusting the learning speed of the average noise spectrum. That is, if the input power is smaller than the noise power, the learning speed is increased because it is likely to be a section containing only noise, and otherwise, the learning speed is reduced because there is a possibility that the section is in a voice section. A coefficient that has an effect.

【００４２】そして、平均ノイズスペクトルの各周波数
の値の総和を求め、これを平均ノイズパワーとする。補
償用ノイズスペクトル、平均ノイズスペクトル、平均ノ
イズパワーは、ノイズスペクトル格納部２５に格納す
る。Then, the sum of the values of each frequency of the average noise spectrum is obtained, and this is set as the average noise power. The compensation noise spectrum, the average noise spectrum, and the average noise power are stored in the noise spectrum storage unit 25.

【００４３】また、上記ノイズ推定処理において、１つ
の周波数のノイズスペクトルを複数の周波数の入力スペ
クトルと対応させれば、ノイズスペクトル格納部２５を
構成するためのＲＡＭ容量を節約することができる。例
として、本実施の形態の２５６ポイントのＦＦＴを用い
る場合に、１つの周波数のノイズスペクトルを４つの周
波数の入力スペクトルから推定するときの、ノイズスペ
クトル格納部２５のＲＡＭ容量を示す。（疑似）振幅ス
ペクトルが周波数軸上で左右対称であることを考慮する
と、全ての周波数で推定する場合は１２８個の周波数の
スペクトルと持続数を格納するので、１２８（周波数）
×２（スペクトルと持続数）×３（補償用の１、２位候
補、平均）で計７６８ＷのＲＡＭ容量が必要になる。Further, in the above-described noise estimation processing, if the noise spectrum of one frequency is made to correspond to the input spectrum of a plurality of frequencies, the RAM capacity for constituting the noise spectrum storage unit 25 can be saved. As an example, when the 256-point FFT of the present embodiment is used, the RAM capacity of the noise spectrum storage unit 25 when estimating a noise spectrum of one frequency from input spectra of four frequencies is shown. Considering that the (pseudo) amplitude spectrum is symmetrical on the frequency axis, when estimating at all frequencies, the spectrum of 128 frequencies and the number of durations are stored, so that 128 (frequency)
× 2 (spectrum and duration) × 3 (1st and 2nd candidates for compensation, average) requires a total of 768 W of RAM capacity.

【００４４】これに対して、１つの周波数のノイズスペ
クトルを４つの周波数の入力スペクトルと対応させる場
合は、３２（周波数）×２（スペクトルと持続数）×３
（補償用の１、２位候補、平均）で計１９２ＷのＲＡＭ
容量でよいことになる。この場合、ノイズスペクトルの
周波数解像度は低下することになるが、上記１対４の場
合は、殆ど性能の劣化がないことを実験により確認して
いる。また、この工夫は、１つの周波数のスペクトルで
ノイズスペクトルを推定するものではないから、定常音
（サイン波、母音等）が長時間続いた場合に、そのスペ
クトルをノイズスペクトルと誤推定することを防ぐ効果
もある。On the other hand, when associating a noise spectrum of one frequency with an input spectrum of four frequencies, 32 (frequency) × 2 (spectrum and the number of durations) × 3
(1st and 2nd candidate for compensation, average) RAM of 192W in total
The capacity is good. In this case, although the frequency resolution of the noise spectrum is reduced, it has been confirmed by an experiment that the performance is hardly degraded in the case of the above-described 1: 4. In addition, since this technique does not estimate a noise spectrum with a spectrum of one frequency, when a stationary sound (sine wave, vowel, etc.) continues for a long time, the spectrum is erroneously estimated as a noise spectrum. It has the effect of preventing.

【００４５】次に、ノイズ削減／スペクトル補償部１８
における処理について説明する。入力スペクトルから、
ノイズスペクトル格納部２５に格納されている平均ノイ
ズスペクトルにノイズ削減係数調節部１４で得られたノ
イズ削減係数を乗じたものを引く（以後、差スペクト
ル）。上記ノイズ推定部２４の説明において示したノイ
ズスペクトル格納部２５のＲＡＭ容量の節約を行った場
合は、入力スペクトルに対応する周波数の平均ノイズス
ペクトルにノイズ削減係数を乗じたものを引く。そし
て、差スペクトルが負になった場合には、ノイズスペク
トル格納部２５に格納された補償用ノイズスペクトルの
１位候補に、ノイズ削減係数調整部１４で求めた補償係
数を乗じたものを代入することにより補償する。これ
を、全ての周波数について行う。また、差スペクトルを
補償した周波数が分るように、周波数毎にフラグデータ
を作成する。例えば、各周波数毎に１つのエリアがあ
り、補償しない時は０、補償したときは１を代入する。
このフラグデータは、差スペクトルと共に、スペクトル
安定化部１９へ送られる。また、フラグデータの値を調
べることによって補償した総数（補償数）を求め、これ
もスペクトル安定部１９へ送る。Next, the noise reduction / spectrum compensation unit 18
Will be described. From the input spectrum,
A value obtained by multiplying the average noise spectrum stored in the noise spectrum storage unit 25 by the noise reduction coefficient obtained by the noise reduction coefficient adjustment unit 14 is subtracted (hereinafter, a difference spectrum). When the RAM capacity of the noise spectrum storage unit 25 described in the description of the noise estimation unit 24 is saved, a value obtained by multiplying the average noise spectrum of the frequency corresponding to the input spectrum by the noise reduction coefficient is subtracted. When the difference spectrum becomes negative, a value obtained by multiplying the first candidate of the compensation noise spectrum stored in the noise spectrum storage unit 25 by the compensation coefficient obtained by the noise reduction coefficient adjustment unit 14 is substituted. To compensate. This is performed for all frequencies. Also, flag data is created for each frequency so that the frequency for which the difference spectrum has been compensated can be found. For example, there is one area for each frequency, and 0 is substituted for no compensation and 1 is substituted for compensation.
This flag data is sent to the spectrum stabilizing unit 19 together with the difference spectrum. Further, the total number of compensations (the number of compensations) is obtained by checking the value of the flag data, and this is also sent to the spectrum stabilizing unit 19.

【００４６】次に、スペクトル安定部１９における処理
について説明する。なお、この処理は、主に音声の含ま
れていない区間の異音感低減のために機能する。Next, the processing in the spectrum stabilizer 19 will be described. This process mainly functions to reduce abnormal noise in a section that does not include voice.

【００４７】まず、ノイズ削減／スペクトル補償部１８
から得られた各周波数の差スペクトルの和を計算し、現
フレームパワーを求める。現フレームパワーは全域と中
域の２種類を求める。全域は全ての周波数（全域と呼
ぶ、本実施の形態では０〜１２８まで）について求め、
中域は聴感的に重要な中ごろの帯域（中域と呼ぶ、本実
施の形態では１６〜７９まで）について求める。First, the noise reduction / spectrum compensation section 18
Is calculated, and the current frame power is obtained. For the current frame power, two types of the whole area and the middle area are obtained. The whole range is obtained for all frequencies (called the whole range, from 0 to 128 in this embodiment),
The middle band is obtained for a middle band that is audibly important (called the middle band, from 16 to 79 in the present embodiment).

【００４８】同様に、ノイズスペクトル格納部２５に格
納された補償用ノイズスペクトルの１位候補についての
和を求め、これを現フレームノイズパワー（全域、中
域）とする。ここで、ノイズ削減／スペクトル補償部１
８から得られた補償数の値を調べ、十分大きい場合、且
つ、以下の３条件のうち少なくとも１つ満たす場合に、
現フレームがノイズのみの区間と判断して、スペクトル
の安定化処理を行う。 （１）入力パワーが、最大パワーに無音検出係数を乗じ
た値よりも小さい。 （２）現フレームパワー（中域）が、現フレームノイズ
パワー（中域）に５．０を乗じた値より小さい。 （３）入力パワーが、ノイズ基準パワーよりも小さい。Similarly, the sum of the first candidate for the compensation noise spectrum stored in the noise spectrum storage unit 25 is obtained, and this is set as the current frame noise power (entire area, middle area). Here, the noise reduction / spectrum compensation unit 1
The value of the compensation number obtained from 8 is checked, and if it is sufficiently large and if at least one of the following three conditions is satisfied,
The current frame is determined to be a section including only noise, and the spectrum is stabilized. (1) The input power is smaller than a value obtained by multiplying the maximum power by a silence detection coefficient. (2) The current frame power (middle frequency) is smaller than the value obtained by multiplying the current frame noise power (middle frequency) by 5.0. (3) The input power is smaller than the noise reference power.

【００４９】安定化処理を行なわない場合は、前スペク
トル格納部２６に格納されたノイズ連続数が正の時に１
を減じ、また現フレームノイズパワー（全域、中域）を
前フレームパワー（全域、中域）とし、それぞれを前ス
ペクトル格納部２６に格納して、位相拡散処理に進む。When the stabilization process is not performed, 1 is set when the number of continuous noises stored in the previous spectrum storage unit 26 is positive.
, And the current frame noise power (entire area, middle area) is set to the previous frame power (entire area, middle area).

【００５０】ここで、スペクトル安定化処理について説
明する。この処理の目的は、無音区間（音声の無いノイ
ズのみの区間）のスペクトルの安定化とパワー低減を実
現することである。処理は２種類あり、ノイズ連続数が
ノイズ基準連続数より小さい場合は（処理１）を、以上
の場合は（処理２）を行なう。２つの処理を以下に示
す。 （処理１）前スペクトル格納部２６に格納されたノイズ
連続数に１を加算し、また現フレームノイズパワー（全
域、中域）を前フレームパワー（全域、中域）とし、そ
れぞれを前スペクトル格納部２６に格納して、位相調整
処理へ進む。 （処理２）前スペクトル格納部２６に格納された前フレ
ームパワー、前フレーム平滑化パワー、更に固定係数で
ある無音パワー減少係数を参照し、（数３）にしたがっ
てそれぞれを変更する。Here, the spectrum stabilization processing will be described. The purpose of this processing is to realize the stabilization of spectrum and the reduction of power in a silent section (a section containing only noise without sound). There are two types of processing. If the number of continuous noises is smaller than the number of continuous noise reference, (Process 1) is performed, and if it is more than that, (Process 2) is performed. The two processes are shown below. (Process 1) One is added to the number of continuous noises stored in the previous spectrum storage unit 26, the current frame noise power (entire area, middle area) is set to the previous frame power (all area, middle area), and each is stored in the previous spectrum. Then, the process proceeds to the phase adjustment process. (Process 2) Referring to the previous frame power, the previous frame smoothing power, and the silence power reduction coefficient, which is a fixed coefficient, stored in the previous spectrum storage unit 26, each is changed according to (Equation 3).

【００５１】[0051]

【数３】 (Equation 3)

【００５２】次に、これらのパワーを差スペクトルに反
映させる。そのために、中域に乗ずる係数（以後、係数
１）と全域に乗ずる係数（以後、係数２）の２つの係数
を算出する。まず、以下の（数４）に示す式で係数１を
算出する。Next, these powers are reflected on the difference spectrum. For this purpose, two coefficients are calculated: a coefficient that multiplies the middle band (hereinafter, coefficient 1) and a coefficient that multiplies the whole band (hereinafter, coefficient 2). First, the coefficient 1 is calculated by the following equation (Equation 4).

【００５３】[0053]

【数４】 (Equation 4)

【００５４】係数２は、係数１の影響を受けるので、求
める手段は多少複雑になる。手順を以下に示す。 （１）前フレーム平滑化パワー（全域）が前フレームパ
ワー（中域）より小さい場合、または、現フレームノイ
ズパワー（全域）が現フレームノイズパワー（中域）よ
りも小さい場合は（２）へ。それ以外の場合は（３）
へ。 （２）係数２は０.０とし、前フレームパワー（全域）
を前フレームパワー（中域）として、（６）へ。 （３）現フレームノイズパワー（全域）が現フレームノ
イズパワー（中域）と等しい場合は（４）へ。異なる場
合は（５）へ。 （４）係数２を１.０とし、（６）へ。 （５）以下の（数５）により係数２を求め、（６）へ。Since the coefficient 2 is affected by the coefficient 1, the means for obtaining the coefficient becomes somewhat complicated. The procedure is shown below. (1) If the previous frame smoothing power (entire region) is smaller than the previous frame power (middle region), or if the current frame noise power (entire region) is smaller than the current frame noise power (middle region), go to (2). . Otherwise (3)
What. (2) The coefficient 2 is set to 0.0, and the previous frame power (entire area)
To (6) as the previous frame power (middle range). (3) If the current frame noise power (entire region) is equal to the current frame noise power (middle region), go to (4). If not, go to (5). (4) Set the coefficient 2 to 1.0, and go to (6). (5) A coefficient 2 is obtained by the following (Equation 5), and the process proceeds to (6).

【００５５】[0055]

【数５】 (Equation 5)

【００５６】（６）係数２算出処理終了。 上記アルゴリズムにより得られた係数１、２は、いずれ
も上限を１.０に、下限を無音パワー減少係数にクリッ
ピングする。そして、中域の周波数（本例では１６〜７
９）の差スペクトルに係数１を乗じて得られた値を差ス
ペクトルとし、さらに、その差スペクトルの全域から中
域を除いた周波数（本例では０〜１５、８０〜１２８）
の差スペクトルに係数２を乗じて得られた値を差スペク
トルとする。それに伴い、前フレームパワー（全域、中
域）を以下の（数６）によって変換する。(6) End of coefficient 2 calculation process. The upper limit of each of the coefficients 1 and 2 obtained by the above algorithm is set to 1.0, and the lower limit is clipped to the silent power reduction coefficient. Then, the frequency in the middle range (16 to 7 in this example)
A value obtained by multiplying the difference spectrum of 9) by a coefficient 1 is defined as a difference spectrum, and further, frequencies (0 to 15, 80 to 128 in this example) obtained by removing the middle band from the entire region of the difference spectrum.
The value obtained by multiplying the difference spectrum of by the coefficient 2 is defined as the difference spectrum. Accordingly, the previous frame power (entire area, middle area) is converted by the following (Equation 6).

【００５７】[0057]

【数６】 (Equation 6)

【００５８】こうして得られた各種パワーデータ等を全
て前スペクトル格納部２６に格納し、（処理２）を終わ
る。The various power data and the like thus obtained are all stored in the previous spectrum storage unit 26, and (Process 2) is completed.

【００５９】以上の要領で、スペクトル安定部１９にお
けるスペクトルの安定化が行われる。In the above manner, the spectrum is stabilized in the spectrum stabilizing section 19.

【００６０】次に、位相調整処理について説明を行う。
従来のスペクトルサブトラクションでは、位相は原則と
して変更しないが、本実施の形態では、その周波数のス
ペクトルが削減時に補償された場合に、位相をランダム
に変更する処理を行なう。この処理により、残ったノイ
ズのランダム性が強くなるので、聴感的に悪印象を与え
にくくなるという効果が得られる。Next, the phase adjustment processing will be described.
In the conventional spectrum subtraction, the phase is not changed in principle, but in the present embodiment, when the spectrum of the frequency is compensated at the time of reduction, the phase is changed randomly. By this processing, the randomness of the remaining noise is increased, so that an effect that it is difficult to give a bad auditory impression is obtained.

【００６１】まず、乱数位相格納部２８に格納された乱
数位相カウンターを得る。そして、全ての周波数のフラ
グデータ（補償の有無を示したデータ）を参照して、補
償している場合は、以下の（数７）により、フーリエ変
換部１７で得られた複素スペクトルの位相を回転させ
る。First, the random number phase counter stored in the random number phase storage unit 28 is obtained. If the compensation is performed by referring to the flag data (data indicating the presence or absence of compensation) of all the frequencies, the phase of the complex spectrum obtained by the Fourier transform unit 17 is calculated by the following (Equation 7). Rotate.

【００６２】[0062]

【数７】 (Equation 7)

【００６３】（数７）においては、２つの乱数位相デー
タをペアで使用している。したがって、上記処理を１回
行なう毎に、乱数位相カウンターを２ずつ増加させ、上
限（本実施の形態では１６）になった場合は０とする。
なお、乱数位相カウンターは乱数位相格納部２７へ格納
し、得られた複素スペクトルは、逆フーリエ変換部２０
へ送る。また、差スペクトルの総和を求め（以下、差ス
ペクトルパワー）、これをスペクトル強調部２１へ送
る。In (Equation 7), two random number phase data are used as a pair. Therefore, every time the above process is performed once, the random number phase counter is incremented by 2 and is set to 0 when the upper limit (16 in the present embodiment) is reached.
Note that the random number phase counter is stored in the random number phase storage unit 27, and the obtained complex spectrum is converted into the inverse Fourier transform unit 20.
Send to In addition, the sum of difference spectra is calculated (hereinafter, difference spectrum power), and is sent to spectrum emphasizing unit 21.

【００６４】逆フーリエ変換部２０では、スペクトル安
定部１９で得られた差スペクトルの振幅と複素スペクト
ルの位相とに基づき、新たな複素スペクトルを構成し、
ＦＦＴを用いて逆フーリエ変換を行う。（得られた信号
を第１次出力信号と呼ぶ。）そして、得られた第１次出
力信号をスペクトル強調部２１へ送る。The inverse Fourier transform unit 20 constructs a new complex spectrum based on the amplitude of the difference spectrum and the phase of the complex spectrum obtained by the spectrum stabilizing unit 19,
Inverse Fourier transform is performed using FFT. (The obtained signal is called a primary output signal.) Then, the obtained primary output signal is sent to the spectrum emphasizing unit 21.

【００６５】次に、スペクトル強調部２１における処理
について説明する。まず、ノイズスペクトル格納部２５
に格納さされた平均ノイズパワーと、スペクトル安定部
１９で得られた差スペクトルパワーと、定数であるノイ
ズ基準パワーを参照して、ＭＡ強調係数とＡＲ強調係数
を選択する。選択は、以下の２つの条件を評価すること
により行う。 （条件１）差スペクトルパワーがノイズスペクトル格納
部２５に格納された平均ノイズパワーに０.６を乗じた
値よりも大きく、且つ、平均ノイズパワーがノイズ基準
パワーよりも大きい。 （条件２）差スペクトルパワーが平均ノイズパワーより
大きい。Next, the processing in the spectrum emphasizing section 21 will be described. First, the noise spectrum storage unit 25
The MA emphasis coefficient and the AR emphasis coefficient are selected with reference to the average noise power stored in, the difference spectrum power obtained by the spectrum stabilizer 19, and the noise reference power that is a constant. The selection is made by evaluating the following two conditions. (Condition 1) The difference spectrum power is greater than the value obtained by multiplying the average noise power stored in the noise spectrum storage unit 25 by 0.6, and the average noise power is greater than the noise reference power. (Condition 2) The difference spectrum power is larger than the average noise power.

【００６６】（条件１）を満たす場合は、これを「有声
区間」とし、ＭＡ強調係数をＭＡ強調係数１ー１とし、
ＡＲ強調係数をＡＲ強調係数１ー１とし、高域強調係数
を高域強調係数１とする。また、（条件１）を満たさ
ず、（条件２）を満たす場合は、これを「無声子音区
間」とし、ＭＡ強調係数をＭＡ強調係数１ー０とし、Ａ
Ｒ強調係数をＡＲ強調係数１ー０とし、高域強調係数を
０とする。また、（条件１）を満たさず、（条件２）を
満たさない場合はこれを「無音区間、ノイズのみの区
間」とし、ＭＡ強調係数をＭＡ強調係数０とし、ＡＲ強
調係数をＡＲ強調係数０とし、高域強調係数を高域強調
係数０とする。When (Condition 1) is satisfied, this is defined as a “voiced section”, the MA emphasis coefficient is set as an MA emphasis coefficient 1-1,
The AR enhancement coefficient is defined as an AR enhancement coefficient 1-1, and the high-frequency enhancement coefficient is defined as a high-frequency enhancement coefficient 1. If (Condition 1) is not satisfied and (Condition 2) is satisfied, this is defined as “unvoiced consonant section”, the MA emphasis coefficient is set to MA emphasis coefficient 1-0, and A
The R emphasis coefficient is set to AR emphasis coefficient 1-0, and the high-frequency emphasis coefficient is set to 0. If (Condition 1) is not satisfied and (Condition 2) is not satisfied, this is referred to as “silent section, section including only noise”, the MA enhancement coefficient is set to MA enhancement coefficient 0, and the AR enhancement coefficient is set to AR enhancement coefficient 0. And the high-frequency emphasis coefficient is set to 0.

【００６７】そして、ＬＰＣ分析部１６から得られた線
形予測係数と、上記ＭＡ強調係数、ＡＲ強調係数を用い
て、以下の（数８）の式に基づき、極強調フィルターの
ＭＡ係数とＡＲ係数とを算出する。Then, using the linear prediction coefficient obtained from the LPC analysis section 16 and the above-mentioned MA emphasis coefficient and AR emphasis coefficient, the MA coefficient and the AR coefficient of the pole emphasis filter are calculated based on the following equation (8). Is calculated.

【００６８】[0068]

【数８】 (Equation 8)

【００６９】そして、逆フーリエ変換部２０において得
られた第１次出力信号に対して、上記ＭＡ係数とＡＲ係
数とを用いて極強調フィルターを掛ける。このフィルタ
ーの伝達関数を、以下の（数９）に示す。Then, the primary output signal obtained in the inverse Fourier transform unit 20 is subjected to a polar enhancement filter using the MA coefficient and the AR coefficient. The transfer function of this filter is shown in (Equation 9) below.

【００７０】[0070]

【数９】 (Equation 9)

【００７１】更に、高域成分を強調するために、上記高
域強調係数を用いて、高域強調フィルターを掛ける。こ
のフィルターの伝達関数を、以下の（数１０）に示す。Further, in order to emphasize the high frequency component, a high frequency emphasis filter is applied using the above high frequency emphasis coefficient. The transfer function of this filter is shown in the following (Equation 10).

【００７２】[0072]

【数１０】 (Equation 10)

【００７３】上記処理によって得られた信号を第２次出
力信号と呼ぶ。なお、フィルターの状態は、スペクトル
強調部２１の内部に保存される。The signal obtained by the above processing is called a secondary output signal. Note that the state of the filter is stored inside the spectrum emphasizing unit 21.

【００７４】最後に、波形整合部２２において、スペク
トル強調部２１で得られた第２次出力信号と、前波形格
納部２８に格納された信号とを、三角窓によって重ね合
せて出力信号を得る。更に、この出力信号の最後の先読
みデータ長分のデータを、前波形格納部２８に格納す
る。このときの整合方法を、以下の（数１１）に示す。Finally, in the waveform matching section 22, the secondary output signal obtained in the spectrum emphasizing section 21 and the signal stored in the previous waveform storage section 28 are superimposed by a triangular window to obtain an output signal. . Further, the last pre-read data length data of the output signal is stored in the pre-waveform storage unit 28. The matching method at this time is shown in (Equation 11) below.

【００７５】[0075]

【数１１】 [Equation 11]

【００７６】ここで注意が必要なのは、出力信号として
は先読みデータ長＋フレーム長分のデータが出力される
が、このうち信号として扱うことができるのは、データ
の始端からフレーム長の長さの区間のみということであ
る。なぜなら、後ろの先読みデータ長のデータは、次の
出力信号を出力するときに書き換えられるからである。
ただし、出力信号の全区間内では連続性は補償されるの
で、ＬＰＣ分析やフィルター分析等の周波数分析には使
用することができる。Here, it should be noted that as the output signal, data corresponding to the pre-read data length + the frame length is output. Among them, the signal which can be treated as the signal is the length of the frame length from the beginning of the data. That is, only the section. This is because the data of the last pre-read data length is rewritten when the next output signal is output.
However, since continuity is compensated in the entire section of the output signal, it can be used for frequency analysis such as LPC analysis and filter analysis.

【００７７】[0077]

【発明の効果】以上のように本発明によれば、音声区間
中でも音声区間外でもノイズスペクトル推定を行うこと
ができ、音声がどのタイミングでデータ中に存在するか
が明らかでない場合でもノイズスペクトルを推定するこ
とができる。また、入力のスペクトル包絡の特徴を線形
予測係数で強調することができ、ノイズレベルが高い場
合でも音質の劣化を防ぐことが出来る。As described above, according to the present invention, the noise spectrum can be estimated both in the voice section and outside the voice section, and the noise spectrum can be estimated even when it is not clear at what timing the voice exists in the data. Can be estimated. In addition, the characteristics of the input spectral envelope can be emphasized by the linear prediction coefficient, so that deterioration in sound quality can be prevented even when the noise level is high.

【００７８】また、ノイズのスペクトルを平均と最低の
２つの方向から推定でき、より的確な削減処理を行うこ
とができる。Also, the noise spectrum can be estimated from the average and the two lowest directions, and more accurate reduction processing can be performed.

【００７９】また、ノイズの平均スペクトルを削減に用
いることによって、より大きくノイズスペクトルを削減
することができ、さらに、補償用スペクトルを別に推定
したことにより、より的確な補償を行うことができる。Further, by using the average spectrum of noise for reduction, the noise spectrum can be greatly reduced, and more accurate compensation can be performed by separately estimating the compensation spectrum.

【００８０】そして、音声の含まれていないノイズのみ
の区間のスペクトルをスムージングすることができ、同
区間のスペクトルが、ノイズ削減のために極端なスペク
トル変動による異音感を防ぐことができる。Then, the spectrum of the section containing only noise without voice can be smoothed, and the spectrum of the section can be prevented from being unusual due to extreme spectral fluctuation for noise reduction.

【００８１】そして、補償された周波数成分の位相にラ
ンダム性を持たせることができ、削減できずに残ったノ
イズを、聴感的に異音感の少ないノイズに変換させるこ
とができる。The phase of the compensated frequency component can be given randomness, and the noise that cannot be reduced can be converted into noise with less audible noise.

【００８２】また、音声区間においては、聴感的により
適当な重み付けができるようになり、無音区間や無声子
音区間においては、聴感重み付けによる異音感を抑える
ことができる。Further, in the voice section, more appropriate weighting can be performed perceptually, and in the silent section or the unvoiced consonant section, abnormal soundness due to the hearing weighting can be suppressed.

【図面の簡単な説明】[Brief description of the drawings]

【図１】本発明の一実施の形態によるノイズ削減装置の
主要部の機能ブロック図FIG. 1 is a functional block diagram of a main part of a noise reduction device according to an embodiment of the present invention.

【図２】従来のスペクトルサブトラクションによるノイ
ズ削減装置の機能ブロック図FIG. 2 is a functional block diagram of a conventional noise reduction device using spectral subtraction.

【符号の説明】 １１ 入力信号 １２ Ａ／Ｄ変換部 １３ ノイズ削減係数格納部 １４ ノイズ削減係数調節部 １５ 入力波形設定部 １６ ＬＰＣ分析部 １７ フーリエ変換部 １８ ノイズ削減／スペクトル補償部 １９ スペクトル安定化部 ２０ 逆フーリエ変換部 ２１ スペクトル強調部 ２２ 波形整合部 ２３ 出力音声 ２４ ノイズ推定部 ２５ ノイズスペクトル格納部 ２６ 前スペクトル格納部 ２７ 乱数位相格納部 ２８ 前波形格納部[Description of Signs] 11 Input signal 12 A / D conversion unit 13 Noise reduction coefficient storage unit 14 Noise reduction coefficient adjustment unit 15 Input waveform setting unit 16 LPC analysis unit 17 Fourier transform unit 18 Noise reduction / spectrum compensation unit 19 Spectrum stabilization Unit 20 inverse Fourier transform unit 21 spectrum emphasis unit 22 waveform matching unit 23 output speech 24 noise estimation unit 25 noise spectrum storage unit 26 previous spectrum storage unit 27 random number phase storage unit 28 previous waveform storage unit

Claims (6)

【特許請求の範囲】[Claims] 【請求項１】 入力音声信号をディジタル信号に変換す
るＡ／Ｄ変換部と、削減量を決定する係数を調節するノ
イズ削減係数調節部と、前記Ａ／Ｄ変換部により得られ
る一定時間長のディジタル信号に対して線形予測分析を
行うＬＰＣ分析部と、前記Ａ／Ｄ変換部により得られる
一定時間長のディジタル信号に対して離散フーリエ変換
を行い入力スペクトルと複素スペクトルを得るフーリエ
変換部と、推定されたノイズのスペクトルを格納するノ
イズスペクトル格納部と、前記フーリエ変換部により得
られる入力スペクトルと前記ノイズスペクトル格納部に
格納されているノイズスペクトルとを比較することによ
ってノイズのスペクトルを推定し、得られたノイズスペ
クトルを前記ノイズスペクトル格納部に格納するノイズ
推定部と、前記ノイズ削減係数調節部により得られる係
数に基づいて前記ノイズスペクトル格納部に格納されて
いるノイズスペクトルを前記フーリエ変換部により得ら
れる入力スペクトルから減じ、更に、得られるスペクト
ルを調べ、減じすぎた周波数のスペクトルを補償するノ
イズ削減／スペクトル補償部と、前記ノイズ削減／スペ
クトル補償部により得られたスペクトルを安定化処理す
るとともに、前記フーリエ変換部により得られた複素ス
ペクトルの位相のうち前記ノイズ削減／スペクトル補償
部において補償された周波数の位相を調整するスペクト
ル安定化部と、前記スペクトル安定化部において安定化
処理されたスペクトルと調整された位相スペクトルとに
基づいて逆フーリエ変換を行う逆フーリエ変換部と、前
記逆フーリエ変換部により得られた信号に対してスペク
トル強調を行うスペクトル強調部と、前記スペクトル強
調部により得られた信号を前のフレームの信号と整合さ
せる波形整合部とを備えることを特徴としたノイズ削減
装置。1. An A / D converter for converting an input audio signal into a digital signal, a noise reduction coefficient adjuster for adjusting a coefficient for determining a reduction amount, and a fixed time length obtained by the A / D converter. An LPC analysis unit that performs a linear prediction analysis on the digital signal, a Fourier transform unit that performs a discrete Fourier transform on the digital signal of a fixed time length obtained by the A / D conversion unit to obtain an input spectrum and a complex spectrum, A noise spectrum storage unit that stores the estimated noise spectrum, and a noise spectrum is estimated by comparing the input spectrum obtained by the Fourier transform unit and the noise spectrum stored in the noise spectrum storage unit. A noise estimation unit that stores the obtained noise spectrum in the noise spectrum storage unit; The noise spectrum stored in the noise spectrum storage unit is subtracted from the input spectrum obtained by the Fourier transform unit based on the coefficient obtained by the noise reduction coefficient adjustment unit, and the obtained spectrum is further examined. A noise reduction / spectrum compensator for compensating the spectrum, a stabilization process on the spectrum obtained by the noise reduction / spectrum compensator, and the noise reduction / spectrum among the phases of the complex spectrum obtained by the Fourier transformer. A spectrum stabilizing unit that adjusts the phase of the frequency compensated in the compensating unit, and an inverse Fourier transform unit that performs an inverse Fourier transform based on the spectrum and the adjusted phase spectrum that have been stabilized in the spectrum stabilizing unit. And the signal obtained by the inverse Fourier transform unit. Spectrum emphasis section and the noise reduction apparatus comprising: a signal waveform matching unit for matching the previous frame signals obtained by the spectrum enhancing section for performing spectrum emphasis against. 【請求項２】 予めノイズ区間であるかどうかの判定を
行ない、ノイズであると判定した場合には、フーリエ変
換部により得られる入力スペクトルを各周波数毎に補償
用ノイズスペクトルと大小比較し、補償用ノイズスペク
トルより小さい場合にその周波数の補償用ノイズスペク
トルを入力スペクトルとすることによって補償用ノイズ
スペクトルを推定し、またそれとは別に、入力スペクト
ルを一定割合で加算していくことによって平均ノイズス
ペクトルを推定し、さらに、補償用ノイズスペクトルと
平均ノイズスペクトルとをノイズスペクトル格納部に格
納するノイズ推定部を備えることを特徴とする請求項１
記載のノイズ削減装置。2. It is determined in advance whether or not the noise section is a noise section. If it is determined that the noise section is a noise section, an input spectrum obtained by a Fourier transform unit is compared in magnitude with a compensation noise spectrum for each frequency, and compensation is performed. If the noise spectrum is smaller than the noise spectrum, the compensation noise spectrum at that frequency is used as the input spectrum to estimate the compensation noise spectrum.Also, by adding the input spectrum at a constant rate, the average noise spectrum is calculated. 2. A noise estimator for estimating and further storing a noise spectrum for compensation and an average noise spectrum in a noise spectrum storage.
The described noise reduction device. 【請求項３】 ノイズ削減係数調節部にて得られたノイ
ズ削減係数をノイズスペクトル格納部に格納された平均
ノイズスペクトルに乗じて、フーリエ変換部にて得られ
た入力スペクトルから減じ、負のスペクトル値になって
しまった周波数に対してはノイズスペクトル格納部に格
納された補償用ノイズスペクトルにより補償するノイズ
削減／スペクトル補償部を備えることを特徴とする請求
項２記載のノイズ削減装置。3. A negative spectrum obtained by multiplying the noise reduction coefficient obtained by the noise reduction coefficient adjusting section by the average noise spectrum stored in the noise spectrum storage section and subtracting from the input spectrum obtained by the Fourier transform section. 3. The noise reduction device according to claim 2, further comprising a noise reduction / spectrum compensation unit that compensates for the frequency that has become a value using a compensation noise spectrum stored in the noise spectrum storage unit. 【請求項４】 ノイズ削減／スペクトル補償部にてノイ
ズ削減とスペクトル補償をなされたスペクトルの全域パ
ワーと聴感的に重要な一部の帯域のパワーとを調べ、入
力された信号が無音区間かどうかを識別し、無音区間と
判断した場合には、全域パワーと中域パワーに対して安
定化処理とパワー低減処理とを行なうスペクトル安定化
部を備えることを特徴とする請求項１記載のノイズ削減
装置。4. A noise reduction / spectrum compensator checks the entire power of the spectrum subjected to noise reduction and spectrum compensation and the power of a part of a band that is perceptually important, and determines whether or not the input signal is a silent section. 2. The noise reduction apparatus according to claim 1, further comprising: a spectrum stabilization unit that performs a stabilization process and a power reduction process on the entire band power and the middle band power when the band is determined to be a silent section. apparatus. 【請求項５】 フーリエ変換部で得られた複素スペクト
ルに対して、ノイズ削減／スペクトル補償部でスペクト
ル補償を受けたかどうかの情報を基に、乱数による位相
回転を行なうスペクトル安定化部を備えることを特徴と
する請求項１記載のノイズ削減装置。5. A spectrum stabilizing unit for performing phase rotation based on random numbers based on information on whether or not a spectrum has been compensated by a noise reduction / spectrum compensating unit for a complex spectrum obtained by a Fourier transform unit. The noise reduction device according to claim 1, wherein: 【請求項６】 予めスペクトル強調に用いる重み係数の
セットを複数用意し、ノイズ削減時には、入力された信
号の状態に応じて重み付け係数のセットを選択し、選択
された重み付け係数を用いてスペクトル強調を行なうス
ペクトル強調部を備えることを特徴とする請求項１記載
のノイズ削減装置。6. A plurality of sets of weighting coefficients used for spectrum enhancement are prepared in advance, and at the time of noise reduction, a set of weighting coefficients is selected according to the state of an input signal, and spectrum enhancement is performed using the selected weighting coefficients. 2. The noise reduction apparatus according to claim 1, further comprising a spectrum emphasis unit that performs the following.
JP31032496A 1996-11-07 1996-11-21 Noise estimation device, noise reduction device, noise estimation method, and noise reduction method Expired - Lifetime JP4006770B2 (en)

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JP31032496A JP4006770B2 (en) 1996-11-21 1996-11-21 Noise estimation device, noise reduction device, noise estimation method, and noise reduction method
CNB97191558XA CN1167047C (en) 1996-11-07 1997-11-06 Sound source vector generation device and method
EP00121445A EP1074977B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method
EP00126851A EP1094447B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method
DE69712537T DE69712537T2 (en) 1996-11-07 1997-11-06 Method for generating a vector quantization code book
CNA2005100714801A CN1677489A (en) 1996-11-07 1997-11-06 Sound source vector generation device, sound encoding device, and sound decoding device
CA2551458A CA2551458C (en) 1996-11-07 1997-11-06 A vector quantization apparatus
DE69723324T DE69723324T2 (en) 1996-11-07 1997-11-06 Method for generating a vector quantization code book
KR1020017001046A KR100339168B1 (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder, and speech decoder
CNB031603556A CN1262994C (en) 1996-11-07 1997-11-06 Sound source vector generator and sound coding device and sound decoding device
US09/101,186 US6453288B1 (en) 1996-11-07 1997-11-06 Method and apparatus for producing component of excitation vector
KR10-2003-7012052A KR20040000406A (en) 1996-11-07 1997-11-06 Modified vector generator
CN2006101007075A CN1877698B (en) 1996-11-07 1997-11-06 Acoustic source vector generator and method
EP00121446A EP1071077B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generator
CNB01132421XA CN1170268C (en) 1996-11-07 1997-11-06 Sound encoding or decoding device and method
EP99126130A EP0992981B1 (en) 1996-11-07 1997-11-06 Excitation Method Generator and Excitation Vector Generating Method
EP01108523A EP1132894B1 (en) 1996-11-07 1997-11-06 Vector quantisation codebook generation method
CNB200310114349XA CN1223994C (en) 1996-11-07 1997-11-06 Sound source vector generator, voice encoder, and voice decoder
CA2614625A CA2614625C (en) 1996-11-07 1997-11-06 A noise canceller for removing a noise component from an input speech signal
DE69712927T DE69712927T2 (en) 1996-11-07 1997-11-06 CELP codec
EP00121460A EP1071079B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method
DE69708693.3T DE69708693C5 (en) 1996-11-07 1997-11-06 Method and apparatus for CELP speech coding or decoding
DE69710794T DE69710794T2 (en) 1996-11-07 1997-11-06 Device for generating a vector quantization codebook
DE69712535T DE69712535T2 (en) 1996-11-07 1997-11-06 Device for generating a vector quantization code book
EP00126299A EP1136985B1 (en) 1996-11-07 1997-11-06 Apparatus and method for CELP speech coding and decoding
DE69721595T DE69721595T2 (en) 1996-11-07 1997-11-06 Method of generating a vector quantization code book
CNB011324228A CN1188833C (en) 1996-11-07 1997-11-06 Acoustic vector generator, and acoustic encoding and decoding device
EP00121447A EP1071078B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method and apparatus
CA2483280A CA2483280C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
EP00121458A EP1074978B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation apparatus
CA002356049A CA2356049C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
AU48842/97A AU4884297A (en) 1996-11-07 1997-11-06 Sound source vector generator, voice encoder, and voice decoder
KR1019980705215A KR100306817B1 (en) 1996-11-07 1997-11-06 Sound source vector generator, voice encoder, and voice decoder
CA002355975A CA2355975C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
CNB011324201A CN1169117C (en) 1996-11-07 1997-11-06 Sound source vector generation device, sound encoding device, and sound decoding device
DE69711715T DE69711715T2 (en) 1996-11-07 1997-11-06 Excitation vector generation
CN2006100799202A CN1845239B (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
EP00121466A EP1071081B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method
CNB011324198A CN1170267C (en) 1996-11-07 1997-11-06 Sound source vector generation device, sound encoding device, and sound decoding device
EP99126131A EP0992982B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method, and speech coding/decoding method and apparatus using the same
EP00126875A EP1085504B1 (en) 1996-11-07 1997-11-06 CELP-Codec
KR1020017010774A KR20030096444A (en) 1996-11-07 1997-11-06 Excitation vector generator and method for generating an excitation vector
DE69712538T DE69712538T2 (en) 1996-11-07 1997-11-06 Method for generating a vector quantization code book
KR1020017001044A KR100326777B1 (en) 1996-11-07 1997-11-06 Generator used with a speech codec and method for generating excitation vector component
CN2011100659405A CN102129862B (en) 1996-11-07 1997-11-06 Noise reduction device and sound encoding device including noise reduction device
CA002355978A CA2355978C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
CA002242345A CA2242345C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
CA002355973A CA2355973C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
DE69708696T DE69708696T2 (en) 1996-11-07 1997-11-06 Method and apparatus for generating a vector quantization code book
EP02000123A EP1217614A1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method
EP99126132A EP0991054B1 (en) 1996-11-07 1997-11-06 A CELP Speech Coder or Decoder, and a Method for CELP Speech Coding or Decoding
HK99102382.4A HK1017472B (en) 1996-11-07 1997-11-06 Sound source vector generator and method for generating a sound source vector
EP99126129A EP0994462B1 (en) 1996-11-07 1997-11-06 Excitation vector generation
EP97911460A EP0883107B9 (en) 1996-11-07 1997-11-06 Sound source vector generator, voice encoder, and voice decoder
CA002356041A CA2356041C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
DE69713633T DE69713633T2 (en) 1996-11-07 1997-11-06 Method for generating a vector quantization code book
CA002356051A CA2356051C (en) 1996-11-07 1997-11-06 Excitation vector generator, speech coder and speech decoder
EP00121464A EP1071080B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method
DE69712539T DE69712539T2 (en) 1996-11-07 1997-11-06 Method and apparatus for generating a vector quantization code book
DE69715478T DE69715478T2 (en) 1996-11-07 1997-11-06 Method and device for CELP speech coding and decoding
DE69708697T DE69708697T2 (en) 1996-11-07 1997-11-06 Method for generating a vector quantization codebook, and apparatus and method for speech coding / decoding
CNB011324244A CN1170269C (en) 1996-11-07 1997-11-06 Sound source vector generation device, sound encoding device, and sound decoding device
DE69712928T DE69712928T2 (en) 1996-11-07 1997-11-06 Method for generating a vector quantization code book
DE69730316T DE69730316T2 (en) 1996-11-07 1997-11-06 SOUND SOURCE GENERATOR, LANGUAGE CODIER AND LANGUAGE DECODER
EP00121467A EP1071082B1 (en) 1996-11-07 1997-11-06 Vector quantization codebook generation method and generator
DE69710505T DE69710505T2 (en) 1996-11-07 1997-11-06 Method and apparatus for generating a vector quantization code book
PCT/JP1997/004033 WO1998020483A1 (en) 1996-11-07 1997-11-06 Sound source vector generator, voice encoder, and voice decoder
CNB011324236A CN1178204C (en) 1996-11-07 1997-11-06 Sound source vector generation device, sound encoding device, and sound decoding device
HK02103544.3A HK1041969B (en) 1996-11-07 1999-05-27 Sound source vector generator, voice encoder and voice decoder
HK02103542.5A HK1041967B (en) 1996-11-07 1999-05-27 Speech coder or decoder and speech coding or decoding method
HK02103543.4A HK1041968B (en) 1996-11-07 1999-05-27 Sound source vector generator, voice encoder and voice decoder
HK02103541.6A HK1041966B (en) 1996-11-07 1999-05-27 Sound source vector generator, voice encoder and voice decoder
HK04107704.8A HK1064788B (en) 1996-11-07 1999-05-27 Noise cancellator
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HK02103546.1A HK1041971B (en) 1996-11-07 1999-05-27 Sound source vector generator, voice encoder and voice decoder
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KR1020017001039A KR100306815B1 (en) 1996-11-07 2001-01-22 Celp speech coder or decoder, and celp speech coding or decoding method
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