Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform - Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment.Comment: 22 pages, 9 figure

Probabilistic Modeling Paradigms for Audio Source Separation

This is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems

Frame Theory for Signal Processing in Psychoacoustics

This review chapter aims to strengthen the link between frame theory and signal processing tasks in psychoacoustics. On the one side, the basic concepts of frame theory are presented and some proofs are provided to explain those concepts in some detail. The goal is to reveal to hearing scientists how this mathematical theory could be relevant for their research. In particular, we focus on frame theory in a filter bank approach, which is probably the most relevant view-point for audio signal processing. On the other side, basic psychoacoustic concepts are presented to stimulate mathematicians to apply their knowledge in this field

Resynthesis of Acoustic Scenes Combining Sound Source Separation and WaveField Synthesis Techniques

[ES] La Separacón de Fuentes ha sido un tema de intensa investigación en muchas aplicaciones de tratamiento de señaal, cubriendo desde el procesado de voz al análisis de im'agenes biomédicas. Aplicando estas técnicas a los sistemas de reproducci'on espacial de audio, se puede solucionar una limitaci ón importante en la resíntesis de escenas sonoras 3D: la necesidad de disponer de las se ñales individuales correspondientes a cada fuente. El sistema Wave-field Synthesis (WFS) puede sintetizar un campo acústico mediante arrays de altavoces, posicionando varias fuentes en el espacio. Sin embargo, conseguir las señales de cada fuente de forma independiente es normalmente un problema. En este trabajo se propone la utilización de distintas técnicas de separaci'on de fuentes sonoras para obtener distintas pistas a partir de grabaciones mono o estéreo. Varios métodos de separación han sido implementados y comprobados, siendo uno de ellos desarrollado por el autor. Aunque los algoritmos existentes están lejos de conseguir una alta calidad, se han realizado tests subjetivos que demuestran cómo no es necesario obtener una separación óptima para conseguir resultados aceptables en la reproducción de escenas 3D[EN] Source Separation has been a subject of intense research in many signal processing applications, ranging from speech processing to medical image analysis. Applied to spatial audio systems, it can be used to overcome one fundamental limitation in 3D scene resynthesis: the need of having the independent signals for each source available. Wave-field Synthesis is a spatial sound reproduction system that can synthesize an acoustic field by means of loudspeaker arrays and it is also capable of positioning several sources in space. However, the individual signals corresponding to these sources must be available and this is often a difficult problem. In this work, we propose to use Sound Source Separation techniques in order to obtain different tracks from stereo and mono mixtures. Some separation methods have been implemented and tested, having been one of them developed by the author. Although existing algorithms are far from getting hi-fi quality, subjective tests show how it is not necessary an optimum separation for getting acceptable results in 3D scene reproductionCobos Serrano, M. (2007). Resynthesis of Acoustic Scenes Combining Sound Source Separation and WaveField Synthesis Techniques. http://hdl.handle.net/10251/12515Archivo delegad