Robust variational Bayesian clustering for underdetermined speech separation

The main focus of this thesis is the enhancement of the statistical framework employed for underdetermined T-F masking blind separation of speech. While humans are capable of extracting a speech signal of interest in the presence of other interference and noise; actual speech recognition systems and hearing aids cannot match this psychoacoustic ability. They perform well in noise and reverberant free environments but suffer in realistic environments. Time-frequency masking algorithms based on computational auditory scene analysis attempt to separate multiple sound sources from only two reverberant stereo mixtures. They essentially rely on the sparsity that binaural cues exhibit in the time-frequency domain to generate masks which extract individual sources from their corresponding spectrogram points to solve the problem of underdetermined convolutive speech separation. Statistically, this can be interpreted as a classical clustering problem. Due to analytical simplicity, a finite mixture of Gaussian distributions is commonly used in T-F masking algorithms for modelling interaural cues. Such a model is however sensitive to outliers, therefore, a robust probabilistic model based on the Student's t-distribution is first proposed to improve the robustness of the statistical framework. This heavy tailed distribution, as compared to the Gaussian distribution, can potentially better capture outlier values and thereby lead to more accurate probabilistic masks for source separation. This non-Gaussian approach is applied to the state-of the-art MESSL algorithm and comparative studies are undertaken to confirm the improved separation quality. A Bayesian clustering framework that can better model uncertainties in reverberant environments is then exploited to replace the conventional expectation-maximization (EM) algorithm within a maximum likelihood estimation (MLE) framework. A variational Bayesian (VB) approach is then applied to the MESSL algorithm to cluster interaural phase differences thereby avoiding the drawbacks of MLE; specifically the probable presence of singularities and experimental results confirm an improvement in the separation performance. Finally, the joint modelling of the interaural phase and level differences and the integration of their non-Gaussian modelling within a variational Bayesian framework, is proposed. This approach combines the advantages of the robust estimation provided by the Student's t-distribution and the robust clustering inherent in the Bayesian approach. In other words, this general framework avoids the difficulties associated with MLE and makes use of the heavy tailed Student's t-distribution to improve the estimation of the soft probabilistic masks at various reverberation times particularly for sources in close proximity. Through an extensive set of simulation studies which compares the proposed approach with other T-F masking algorithms under different scenarios, a significant improvement in terms of objective and subjective performance measures is achieved

Use of Bimodal Coherence to Resolve Spectral Indeterminacy in Convolutive BSS

Recent studies show that visual information contained in visual speech can be helpful for the performance enhancement of audio-only blind source separation (BSS) algorithms. Such information is exploited through the statistical characterisation of the coherence between the audio and visual speech using, e.g. a Gaussian mixture model (GMM). In this paper, we present two new contributions. An adapted expectation maximization (AEM) algorithm is proposed in the training process to model the audio-visual coherence upon the extracted features. The coherence is exploited to solve the permutation problem in the frequency domain using a new sorting scheme. We test our algorithm on the XM2VTS multimodal database. The experimental results show that our proposed algorithm outperforms traditional audio-only BSS

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

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