Yi et al., 2021 - Google Patents
Music genre classification with LSTM based on time and frequency domain featuresYi et al., 2021
- Document ID
- 15618966845675791939
- Author
- Yi Y
- Zhu X
- Yue Y
- Wang W
- Publication year
- Publication venue
- 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)
External Links
Snippet
Deep features generated from deep learning models contain more information for music classification than short-term features. This paper uses a long-short term memory (LSTM) model to generate deep features and achieve music genre classification. Firstly, two short …
- 230000015654 memory 0 abstract description 8
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3074—Audio data retrieval
- G06F17/30743—Audio data retrieval using features automatically derived from the audio content, e.g. descriptors, fingerprints, signatures, MEP-cepstral coefficients, musical score, tempo
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3074—Audio data retrieval
- G06F17/30755—Query formulation specially adapted for audio data retrieval
- G06F17/30758—Query by example, e.g. query by humming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3074—Audio data retrieval
- G06F17/30749—Audio data retrieval using information manually generated or using information not derived from the audio data, e.g. title and artist information, time and location information, usage information, user ratings
- G06F17/30752—Audio data retrieval using information manually generated or using information not derived from the audio data, e.g. title and artist information, time and location information, usage information, user ratings using information manually generated, e.g. tags, keywords, comments, title or artist information, time, location or usage information, user ratings
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30017—Multimedia data retrieval; Retrieval of more than one type of audiovisual media
- G06F17/30023—Querying
- G06F17/30029—Querying by filtering; by personalisation, e.g. querying making use of user profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
- G10H2210/00—Aspects or methods of musical processing having intrinsic musical character, i.e. involving musical theory or musical parameters or relying on musical knowledge, as applied in electrophonic musical tools or instruments
- G10H2210/031—Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L17/00—Speaker identification or verification
- G10L17/26—Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signal, using source filter models or psychoacoustic analysis
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
- G10H2240/00—Data organisation or data communication aspects, specifically adapted for electrophonic musical tools or instruments
- G10H2240/121—Musical libraries, i.e. musical databases indexed by musical parameters, wavetables, indexing schemes using musical parameters, musical rule bases or knowledge bases, e.g. for automatic composing methods
- G10H2240/131—Library retrieval, i.e. searching a database or selecting a specific musical piece, segment, pattern, rule or parameter set
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20220036869A1 (en) | Audio processing techniques for semantic audio recognition and report generation | |
Yi et al. | Music genre classification with LSTM based on time and frequency domain features | |
Fu et al. | A survey of audio-based music classification and annotation | |
Zhang | Music feature extraction and classification algorithm based on deep learning | |
US10043500B2 (en) | Method and apparatus for making music selection based on acoustic features | |
US9158760B2 (en) | Audio decoding with supplemental semantic audio recognition and report generation | |
US9183849B2 (en) | Audio matching with semantic audio recognition and report generation | |
Niyazov et al. | Content-based music recommendation system | |
Hu et al. | Separation of singing voice using nonnegative matrix partial co-factorization for singer identification | |
Roche et al. | Make that sound more metallic: Towards a perceptually relevant control of the timbre of synthesizer sounds using a variational autoencoder | |
Cotton et al. | Soundtrack classification by transient events | |
Balachandra et al. | Music genre classification for indian music genres | |
Ghosal et al. | Song/instrumental classification using spectrogram based contextual features | |
Arumugam et al. | An efficient approach for segmentation, feature extraction and classification of audio signals | |
Amarasinghe et al. | Supervised learning approach for singer identification in sri lankan music | |
Cheng | Music information retrieval technology: Fusion of music, artificial intelligence and blockchain | |
Chen et al. | Cross-cultural music emotion recognition by adversarial discriminative domain adaptation | |
Pham et al. | Hit song prediction based on gradient boosting decision tree | |
Ranjan et al. | Oktoechos classification and generation of liturgical music using deep learning frameworks | |
Simas Filho et al. | Genre classification for brazilian music using independent and discriminant features | |
Muthumari | Classification Analysis for Musical Instrument Signal | |
Arkachaari et al. | Bi-sampling approach to classify music mood leveraging raga-rasa association in Indian classical music | |
Kraljević et al. | Cochlea-based features for music emotion classification | |
Rajesh | Deep Autoencoder-based Framework for Robust Singer Identification in Music Analysis | |
Schmidt | Reproduction of Black-Box music analysis algorithms through machine learning |