Acoustical Engineering

Beamforming is an acoustic imaging technique that allows the visualization of sound scenes via the generation of acoustic images, which can be treated as maps. The technique works with the processing of signals acquired with an array of microphones, therefore requiring a computational implementation to be applied. Currently, there are several toolboxes to work with the technique, each with its unique implementation characteristics — including peculiarities of their different application fields. On that account, it would be fruitful to combine such tools, extracting and using their specialized parts. This work proposes and describes the development of a new set of tools that allows the integration between such toolboxes. It aims to facilitate the combined use and the sharing of inputs and outputs. The toolbox under development helps communicating between the beamforming tools Acoular, Beamap, and the tool within the ITA Toolbox — with the first one being in Python and the last two in Matlab language. Furthermore, it provides a connection between Acoular and Amiet Tools, the latter focused on aeroacoustics — simplifying the sophisticated creation of acoustic maps for simulated noise prediction data caused by the interaction of a turbulent velocity field with the leading edge of an airfoil. This article clarifies development details, as well as demonstrates computational results of beamforming, obtained with the integration between tools. The study is part of the developments of the research group in signal processing and acoustic imaging of the Acoustical Engineering of the Federal University of Santa Maria (UFSM), in south Brazil. Resumo: Beamforming é uma técnica de imageamento acústico que permite visualizar cenários sonoros por meio da geração de imagens acústicas, as quais podem ser tratadas como mapas. A técnica trabalha com o processamento de sinais adquiridos via a utilização de um arranjo de microfones, dessa maneira, necessitando de implementação computacional para que possa ser aplicada. Atualmente existem diversas toolboxes (ou conjunto de ferramentas) para trabalhar com a técnica, cada uma traz suas características únicas de implementação — podendo incluir peculiaridades de suas distintas áreas de atuação. Desse modo, seria frutífero poder combinar tais ferramentas, extraindo e utilizando a sua parte aprimorada. Este trabalho propõe e descreve o desenvolvimento de um novo conjunto de ferramentas que permite a integração entre toolboxes, trazendo consigo a facilitação da utilização e de compartilhamento de entradas e saídas. A toolbox em desenvolvimento facilita a comunicação entre as ferramentas de beamforming Acoular, Beamap e aquela dentro do ITA Toolbox — sendo a primeira em linguagem Python e as duas últimas em Matlab. Além disso, propicia a conexão entre o Acoular e o Amiet Tools, este focado em aeroacústica, a fim de simplificar a sofisticada criação de mapas acústicos para dados simulados de predição de ruído causado pela interação da turbulência com aerofólios. O presente artigo aclara detalhes de desenvolvimento, bem como demonstra resultados computais de beamforming, obtidos nas integrações entre ferramentas. O estudo faz parte dos desenvolvimentos do grupo de pesquisa em processamento de sinais e imageamento acústico da Engenharia Acústica da Universidade Federal de Santa Maria (UFSM).

The aim of this work is use data fusion in a set of data obtained from measurements done with a microphone array in different times to improve beamforming results. Beamforming is a technique that basically samples the sound field with an array of sensors. The correct summation of these signals will render a reinforcement of the recorded sound for a chosen direction in space. In addition, processing a set of possible incoming directions enables the creation of sound maps. The spatial resolution in beamforming is directly related to array's constructive factors and frequency of analysis. One way to improve resolution is increasing array's size and number of sensors. Considering the measured source statistically stationary, it is possible to use signals obtained in different times to evaluate it. In this way, the array can be placed in different positions, and the data acquired can be processed and fused in order to create a single set of data corresponding to a virtual array composed by all aforementioned positions.

A simplified measurement object to investigate different methods for the analysis and synthesis of "Transfer Paths" was developed. This object consists of a five-sided wooden box and covers of diferente materials, which can be easily exchanged to vary the situation. This allows the investigation of the acoustic transmission from sources within and outside the box. Furthermore, vibroacoustic paths can be investigated by connecting vibrating sound sources to the covers or the sidewalls. The relatively simple construction allows for the realization of different measurement scenarios. Thus different institutions can work in their own labs with their own measurement systems and measurements still remain comparable. Currently, three research institutes work together in this project. This article describes the construction of the box and the relevance of this scientific research. It also concentrates on the consistency of measurements of transfer functions and/or acoustic parameters among different research groups. Finally measurement results from airborne transfer paths will be compared and discussed regarding their deviations.

Beamforming techniques use microphone arrays to detect prominent sound sources, also providing the direction of arrival (DOA) of each given sound source. Usually, source maps are then analyzed to find an acoustic optimization of a product, mostly by reducing the influence of the main sources in terms of sound pressure levels. A common tool for the subjective evaluation of products in terms of sound quality is the binaural recording with a dummy head. This work aims to combine these two approaches by using data from microphone array measurements to simulate binaural signals that would be acquired by an artificial head at the position of the array. The auralization and therefore simulation of the dummy head is obtained by spatial filtering of known sound sources with directionally dependent filters from a head related transfer functions (HRTF) database. The positions of the different sound sources are obtained by source localization methods. Since the position of the object can be roughly estimated, unwanted noise sources from different directions can be suppressed by this method. By considering moving sources, the common techniques of linear-time invariant systems are not applicable anymore. The DOA has to be repeatedly estimated and the spatial filtering adapted accordingly. This paper proposes two different approaches for the offline auralization of beamforming measurements. The results are discussed regarding audible artifacts due to the movement of the sources and the 3D impression of the auralized events.