Joint source localization and dereverberation by sound field interpolation using sparse regularization

In this paper, source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists in the interpolation of the sound field measured by a set of microphones by matching the recorded sound pressure with that of a particular acoustic model. This model is based on a collection of equivalent sources creating either spherical or plane waves. In order to achieve meaningful results, spatial, spatio-temporal and spatio-spectral sparsity can be promoted in the signals originating from the equivalent sources. The inverse problem consists of a large-scale optimization problem that is solved using a first order matrix-free optimization algorithm. It is shown that once the equivalent source signals capable of effectively interpolating the sound field are obtained, they can be readily used to localize a speech sound source in terms of Direction of Arrival (DOA) and to perform dereverberation in a highly reverberant environment

ベイズ法によるマイクロフォンアレイ処理

京都大学0048新制・課程博士博士(情報学)甲第18412号情博第527号新制||情||93(附属図書館)31270京都大学大学院情報学研究科知能情報学専攻(主査)教授 奥乃 博, 教授 河原 達也, 准教授 CUTURI CAMETO Marco, 講師 吉井 和佳学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Fast Numerical and Machine Learning Algorithms for Spatial Audio Reproduction

Audio reproduction technologies have underwent several revolutions from a purely mechanical, to electromagnetic, and into a digital process. These changes have resulted in steady improvements in the objective qualities of sound capture/playback on increasingly portable devices. However, most mobile playback devices remove important spatial-directional components of externalized sound which are natural to the subjective experience of human hearing. Fortunately, the missing spatial-directional parts can be integrated back into audio through a combination of computational methods and physical knowledge of how sound scatters off of the listener's anthropometry in the sound-field. The former employs signal processing techniques for rendering the sound-field. The latter employs approximations of the sound-field through the measurement of so-called Head-Related Impulse Responses/Transfer Functions (HRIRs/HRTFs). This dissertation develops several numerical and machine learning algorithms for accelerating and personalizing spatial audio reproduction in light of available mobile computing power. First, spatial audio synthesis between a sound-source and sound-field requires fast convolution algorithms between the audio-stream and the HRIRs. We introduce a novel sparse decomposition algorithm for HRIRs based on non-negative matrix factorization that allows for faster time-domain convolution than frequency-domain fast-Fourier-transform variants. Second, the full sound-field over the spherical coordinate domain must be efficiently approximated from a finite collection of HRTFs. We develop a joint spatial-frequency covariance model for Gaussian process regression (GPR) and sparse-GPR methods that supports the fast interpolation and data fusion of HRTFs across multiple data-sets. Third, the direct measurement of HRTFs requires specialized equipment that is unsuited for widespread acquisition. We ``bootstrap'' the human ability to localize sound in listening tests with Gaussian process active-learning techniques over graphical user interfaces that allows the listener to infer his/her own HRTFs. Experiments are conducted on publicly available HRTF datasets and human listeners

Spatial Acoustic Vector Based Sound Field Reproduction

Spatial sound field reproduction aims to recreate an immersive sound field over a spatial region. The existing sound pressure based approaches to spatial sound field reproduction focus on the accurate approximation of original sound pressure over space, which ignores the perceptual accuracy of the reproduced sound field. The acoustic vectors of particle velocity and sound intensity appear to be closely linked with human perception of sound localization in literature. Therefore, in this thesis, we explore the spatial distributions of the acoustic vectors, and seek to develop algorithms to perceptually reproduce the original sound field over a continuous spatial region based on the vectors. A theory of spatial acoustic vectors is first developed, where the spatial distributions of particle velocity and sound intensity are derived from sound pressure. To extract the desired sound pressure from a mixed sound field environment, a 3D sound field separation technique is also formulated. Based on this theory, a series of reproduction techniques are proposed to improve the perceptual performance. The outcomes resulting from this theory are: (i) derivation of a particle velocity assisted 3D sound field reproduction technique which allows for non-uniform loudspeaker geometry with a limited number of loudspeakers, (ii) design of particle velocity based mixed-source sound field translation technique for binaural reproduction that can provide sound field translation with good perceptual experience over a large space, (iii) derivation of an intensity matching technique that can reproduce the desired sound field in a spherical region by controlling the sound intensity on the surface of the region, and (iv) two intensity based multizone sound field reproduction algorithms that can reproduce the desired sound field over multiple spatial zones. Finally, these techniques are evaluated by comparing to the conventional approaches through numerical simulations and real-world experiments

A room acoustics measurement system using non-invasive microphone arrays

This thesis summarises research into adaptive room correction for small rooms and pre-recorded material, for example music of films. A measurement system to predict the sound at a remote location within a room, without a microphone at that location was investigated. This would allow the sound within a room to be adaptively manipulated to ensure that all listeners received optimum sound, therefore increasing their enjoyment. The solution presented used small microphone arrays, mounted on the room's walls. A unique geometry and processing system was designed, incorporating three processing stages, temporal, spatial and spectral. The temporal processing identifies individual reflection arrival times from the recorded data. Spatial processing estimates the angles of arrival of the reflections so that the three-dimensional coordinates of the reflections' origin can be calculated. The spectral processing then estimates the frequency response of the reflection. These estimates allow a mathematical model of the room to be calculated, based on the acoustic measurements made in the actual room. The model can then be used to predict the sound at different locations within the room. A simulated model of a room was produced to allow fast development of algorithms. Measurements in real rooms were then conducted and analysed to verify the theoretical models developed and to aid further development of the system. Results from these measurements and simulations, for each processing stage are presented

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Deep Prior-Based Audio Inpainting Using Multi-Resolution Harmonic Convolutional Neural Networks

In this manuscript, we propose a novel method to perform audio inpainting, i.e., the restoration of audio signals presenting multiple missing parts. Audio inpainting can be interpreted in the context of inverse problems as the task of reconstructing an audio signal from its corrupted observation. For this reason, our method is based on a deep prior approach, a recently proposed technique that proved to be effective in the solution of many inverse problems, among which image inpainting. Deep prior allows one to consider the structure of a neural network as an implicit prior and to adopt it as a regularizer. Differently from the classical deep learning paradigm, deep prior performs a single-element training and thus it can be applied to corrupted audio signals independently from the available training data sets. In the context of audio inpainting, a network presenting relevant audio priors will possibly generate a restored version of an audio signal, only provided with its corrupted observation. Our method exploits a time-frequency representation of audio signals and makes use of a multi-resolution convolutional autoencoder, that has been enhanced to perform the harmonic convolution operation. Results show that the proposed technique is able to provide a coherent and meaningful reconstruction of the corrupted audio. It is also able to outperform the methods considered for comparison, in its domain of application