Performance analysis of dynamic acoustic source separation in reverberant rooms

Published versio

Multimodal methods for blind source separation of audio sources

The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance. [Continues.

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

A frequency-based BSS technique for speech source separation.

Ngan Lai Yin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 95-100).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Blind Signal Separation (BSS) Methods --- p.4Chapter 1.2 --- Objectives of the Thesis --- p.6Chapter 1.3 --- Thesis Outline --- p.8Chapter 2 --- Blind Adaptive Frequency-Shift (BA-FRESH) Filter --- p.9Chapter 2.1 --- Cyclostationarity Properties --- p.10Chapter 2.2 --- Frequency-Shift (FRESH) Filter --- p.11Chapter 2.3 --- Blind Adaptive FRESH Filter --- p.12Chapter 2.4 --- Reduced-Rank BA-FRESH Filter --- p.14Chapter 2.4.1 --- CSP Method --- p.14Chapter 2.4.2 --- PCA Method --- p.14Chapter 2.4.3 --- Appropriate Choice of Rank --- p.14Chapter 2.5 --- Signal Extraction of Spectrally Overlapped Signals --- p.16Chapter 2.5.1 --- Simulation 1: A Fixed Rank --- p.17Chapter 2.5.2 --- Simulation 2: A Variable Rank --- p.18Chapter 2.6 --- Signal Separation of Speech Signals --- p.20Chapter 2.7 --- Chapter Summary --- p.22Chapter 3 --- Reverberant Environment --- p.23Chapter 3.1 --- Small Room Acoustics Model --- p.23Chapter 3.2 --- Effects of Reverberation to Speech Recognition --- p.27Chapter 3.2.1 --- Short Impulse Response --- p.27Chapter 3.2.2 --- Small Room Impulse Response Modelled by Image Method --- p.32Chapter 3.3 --- Chapter Summary --- p.34Chapter 4 --- Information Theoretic Approach for Signal Separation --- p.35Chapter 4.1 --- Independent Component Analysis (ICA) --- p.35Chapter 4.1.1 --- Kullback-Leibler (K-L) Divergence --- p.37Chapter 4.2 --- Information Maximization (Infomax) --- p.39Chapter 4.2.1 --- Stochastic Gradient Descent and Stability Problem --- p.41Chapter 4.2.2 --- Infomax and ICA --- p.41Chapter 4.2.3 --- Infomax and Maximum Likelihood --- p.42Chapter 4.3 --- Signal Separation by Infomax --- p.43Chapter 4.4 --- Chapter Summary --- p.45Chapter 5 --- Blind Signal Separation (BSS) in Frequency Domain --- p.47Chapter 5.1 --- Convolutive Mixing System --- p.48Chapter 5.2 --- Infomax in Frequency Domain --- p.52Chapter 5.3 --- Adaptation Algorithms --- p.54Chapter 5.3.1 --- Standard Gradient Method --- p.54Chapter 5.3.2 --- Natural Gradient Method --- p.55Chapter 5.3.3 --- Convergence Performance --- p.56Chapter 5.4 --- Subband Adaptation --- p.57Chapter 5.5 --- Energy Weighting --- p.59Chapter 5.6 --- The Permutation Problem --- p.61Chapter 5.7 --- Performance Evaluation --- p.63Chapter 5.7.1 --- De-reverberation Performance Factor --- p.63Chapter 5.7.2 --- De-Noise Performance Factor --- p.63Chapter 5.7.3 --- Spectral Signal-to-noise Ratio (SNR) --- p.65Chapter 5.8 --- Chapter Summary --- p.65Chapter 6 --- Simulation Results and Performance Analysis --- p.67Chapter 6.1 --- Small Room Acoustics Modelled by Image Method --- p.67Chapter 6.2 --- Signal Sources --- p.68Chapter 6.2.1 --- Cantonese Speech --- p.69Chapter 6.2.2 --- Noise --- p.69Chapter 6.3 --- De-Noise and De-Reverberation Performance Analysis --- p.69Chapter 6.3.1 --- Speech and White Noise --- p.73Chapter 6.3.2 --- Speech and Voice Babble Noise --- p.76Chapter 6.3.3 --- Two Female Speeches --- p.79Chapter 6.4 --- Recognition Accuracy Performance Analysis --- p.83Chapter 6.4.1 --- Speech and White Noise --- p.83Chapter 6.4.2 --- Speech and Voice Babble Noise --- p.84Chapter 6.4.3 --- Two Cantonese Speeches --- p.85Chapter 6.5 --- Chapter Summary --- p.87Chapter 7 --- Conclusions and Suggestions for Future Research --- p.88Chapter 7.1 --- Conclusions --- p.88Chapter 7.2 --- Suggestions for Future Research --- p.91Appendices --- p.92A The Proof of Stability Conditions for Stochastic Gradient De- scent Algorithm (Ref. (4.15)) --- p.92Bibliography --- p.9

Enhanced independent vector analysis for speech separation in room environments

PhD ThesisThe human brain has the ability to focus on a desired sound source in the presence of several active sound sources. The machine based method lags behind in mimicking this particular skill of human beings. In the domain of digital signal processing this problem is termed as the cocktail party problem. This thesis thus aims to further the eld of acoustic source separation in the frequency domain based on exploiting source independence. The main challenge in such frequency domain algorithms is the permutation problem. Independent vector analysis (IVA) is a frequency domain blind source separation algorithm which can theoretically obviate the permutation problem by preserving the dependency structure within each source vector whilst eliminating the dependency between the frequency bins of di erent source vectors. This thesis in particular focuses on improving the separation performance of IVA algorithms which are used for frequency domain acoustic source separation in real room environments. The source prior is crucial to the separation performance of the IVA algorithm as it is used to model the nonlinear dependency structure within the source vectors. An alternative multivariate Student's t distribution source prior is proposed for the IVA algorithm as it is known to be well suited for modelling certain speech signals due to its heavy tail nature. Therefore the nonlinear score function that is derived from the proposed Student's t source prior can better model the dependency structure within the frequency bins and thereby enhance the separation performance and the convergence speed of the IVA and the Fast version of the IVA (FastIVA) algorithms. 4 5 A novel energy driven mixed Student's t and the original super Gaussian source prior is also proposed for the IVA algorithms. As speech signals can be composed of many high and low amplitude data points, therefore the Student's t distribution in the mixed source prior can account for the high amplitude data points whereas the original su- per Gaussian distribution can cater for the other information in the speech signals. Furthermore, the weight of both distributions in the mixed source prior can be ad- justed according to the energy of the observed mixtures. Therefore the mixed source prior adapts the measured signals and further enhances the performance of the IVA algorithm. A common approach within the IVA algorithm is to model di erent speech sources with an identical source prior, however this does not account for the unique characteristics of each speech signal. Therefore dependency modelling for di erent speech sources can be improved by modelling di erent speech sources with di erent source priors. Hence, the Student's t mixture model (SMM) is introduced as a source prior for the IVA algorithm. This new source prior can adapt according to the nature of di erent speech signals and the parameters for the proposed SMM source prior are estimated by deriving an e cient expectation maximization (EM) algorithm. As a result of this study, a novel EM framework for the IVA algorithm with the SMM as a source prior is proposed which is capable of separating the sources in an e cient manner. The proposed algorithms are tested in various realistic reverberant room environments with real speech signals. All the experiments and evaluation demonstrate the robustness and enhanced separation performance of the proposed algorithms

End-to-end Speech Separation with Neural Networks

Speech separation has long been an active research topic in the signal processing community with its importance in a wide range of applications such as hearable devices and telecommunication systems. It not only serves as a fundamental problem for all higher-level speech processing tasks such as automatic speech recognition, natural language understanding, and smart personal assistants, but also plays an important role in smart earphones and augmented and virtual reality devices. With the recent progress in deep neural networks, the separation performance has been significantly advanced by various new problem definitions and model architectures. The most widely-used approach in the past years performs separation in time-frequency domain, where a spectrogram or a time-frequency representation is first calculated from the mixture signal and multiple time-frequency masks are then estimated for the target sources. The masks are applied on the mixture's time-frequency representation to extract the target representations, and then operations such as inverse short-time Fourier transform is utilized to convert them back to waveforms. However, such frequency-domain methods may have difficulties in modeling the phase spectrogram as the conventional time-frequency masks often only consider the magnitude spectrogram. Moreover, the training objectives for the frequency-domain methods are typically also in frequency-domain, which may not be inline with widely-used time-domain evaluation metrics such as signal-to-noise ratio and signal-to-distortion ratio. The problem formulation of time-domain, end-to-end speech separation naturally arises to tackle the disadvantages in the frequency-domain systems. The end-to-end speech separation networks take the mixture waveform as input and directly estimate the waveforms of the target sources. Following the general pipeline of conventional frequency-domain systems which contains a waveform encoder, a separator, and a waveform decoder, time-domain systems can be design in a similar way while significantly improves the separation performance. In this dissertation, I focus on multiple aspects in the general problem formulation of end-to-end separation networks including the system designs, model architectures, and training objectives. I start with a single-channel pipeline, which we refer to as the time-domain audio separation network (TasNet), to validate the advantage of end-to-end separation comparing with the conventional time-frequency domain pipelines. I then move to the multi-channel scenario and introduce the filter-and-sum network (FaSNet) for both fixed-geometry and ad-hoc geometry microphone arrays. Next I introduce methods for lightweight network architecture design that allows the models to maintain the separation performance while using only as small as 2.5% model size and 17.6% model complexity. After that, I look into the training objective functions for end-to-end speech separation and describe two training objectives for separating varying numbers of sources and improving the robustness under reverberant environments, respectively. Finally I take a step back and revisit several problem formulations in end-to-end separation pipeline and raise more questions in this framework to be further analyzed and investigated in future works

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo