Independent Component Analysis Enhancements for Source Separation in Immersive Audio Environments

In immersive audio environments with distributed microphones, Independent Component Analysis (ICA) can be applied to uncover signals from a mixture of other signals and noise, such as in a cocktail party recording. ICA algorithms have been developed for instantaneous source mixtures and convolutional source mixtures. While ICA for instantaneous mixtures works when no delays exist between the signals in each mixture, distributed microphone recordings typically result various delays of the signals over the recorded channels. The convolutive ICA algorithm should account for delays; however, it requires many parameters to be set and often has stability issues. This thesis introduces the Channel Aligned FastICA (CAICA), which requires knowledge of the source distance to each microphone, but does not require knowledge of noise sources. Furthermore, the CAICA is combined with Time Frequency Masking (TFM), yielding even better SOI extraction even in low SNR environments. Simulations were conducted for ranking experiments tested the performance of three algorithms: Weighted Beamforming (WB), CAICA, CAICA with TFM. The Closest Microphone (CM) recording is used as a reference for all three. Statistical analyses on the results demonstrated superior performance for the CAICA with TFM. The algorithms were applied to experimental recordings to support the conclusions of the simulations. These techniques can be deployed in mobile platforms, used in surveillance for capturing human speech and potentially adapted to biomedical fields

Convolutive Blind Source Separation Methods

In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks

Audio Source Separation Using Sparse Representations

This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 10, pp. 246-264. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch010file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04The authors address the problem of audio source separation, namely, the recovery of audio signals from recordings of mixtures of those signals. The sparse component analysis framework is a powerful method for achieving this. Sparse orthogonal transforms, in which only few transform coefficients differ significantly from zero, are developed; once the signal has been transformed, energy is apportioned from each transform coefficient to each estimated source, and, finally, the signal is reconstructed using the inverse transform. The overriding aim of this chapter is to demonstrate how this framework, as exemplified here by two different decomposition methods which adapt to the signal to represent it sparsely, can be used to solve different problems in different mixing scenarios. To address the instantaneous (neither delays nor echoes) and underdetermined (more sources than mixtures) mixing model, a lapped orthogonal transform is adapted to the signal by selecting a basis from a library of predetermined bases. This method is highly related to the windowing methods used in the MPEG audio coding framework. In considering the anechoic (delays but no echoes) and determined (equal number of sources and mixtures) mixing case, a greedy adaptive transform is used based on orthogonal basis functions that are learned from the observed data, instead of being selected from a predetermined library of bases. This is found to encode the signal characteristics, by introducing a feedback system between the bases and the observed data. Experiments on mixtures of speech and music signals demonstrate that these methods give good signal approximations and separation performance, and indicate promising directions for future research

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

Informed algorithms for sound source separation in enclosed reverberant environments

While humans can separate a sound of interest amidst a cacophony of contending sounds in an echoic environment, machine-based methods lag behind in solving this task. This thesis thus aims at improving performance of audio separation algorithms when they are informed i.e. have access to source location information. These locations are assumed to be known a priori in this work, for example by video processing. Initially, a multi-microphone array based method combined with binary time-frequency masking is proposed. A robust least squares frequency invariant data independent beamformer designed with the location information is utilized to estimate the sources. To further enhance the estimated sources, binary time-frequency masking based post-processing is used but cepstral domain smoothing is required to mitigate musical noise. To tackle the under-determined case and further improve separation performance at higher reverberation times, a two-microphone based method which is inspired by human auditory processing and generates soft time-frequency masks is described. In this approach interaural level difference, interaural phase difference and mixing vectors are probabilistically modeled in the time-frequency domain and the model parameters are learned through the expectation-maximization (EM) algorithm. A direction vector is estimated for each source, using the location information, which is used as the mean parameter of the mixing vector model. Soft time-frequency masks are used to reconstruct the sources. A spatial covariance model is then integrated into the probabilistic model framework that encodes the spatial characteristics of the enclosure and further improves the separation performance in challenging scenarios i.e. when sources are in close proximity and when the level of reverberation is high. Finally, new dereverberation based pre-processing is proposed based on the cascade of three dereverberation stages where each enhances the twomicrophone reverberant mixture. The dereverberation stages are based on amplitude spectral subtraction, where the late reverberation is estimated and suppressed. The combination of such dereverberation based pre-processing and use of soft mask separation yields the best separation performance. All methods are evaluated with real and synthetic mixtures formed for example from speech signals from the TIMIT database and measured room impulse responses