Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function

This paper addresses the problem of speech separation and enhancement from multichannel convolutive and noisy mixtures, \emph{assuming known mixing filters}. We propose to perform the speech separation and enhancement task in the short-time Fourier transform domain, using the convolutive transfer function (CTF) approximation. Compared to time-domain filters, CTF has much less taps, consequently it has less near-common zeros among channels and less computational complexity. The work proposes three speech-source recovery methods, namely: i) the multichannel inverse filtering method, i.e. the multiple input/output inverse theorem (MINT), is exploited in the CTF domain, and for the multi-source case, ii) a beamforming-like multichannel inverse filtering method applying single source MINT and using power minimization, which is suitable whenever the source CTFs are not all known, and iii) a constrained Lasso method, where the sources are recovered by minimizing the $\ell_1$-norm to impose their spectral sparsity, with the constraint that the $\ell_2$-norm fitting cost, between the microphone signals and the mixing model involving the unknown source signals, is less than a tolerance. The noise can be reduced by setting a tolerance onto the noise power. Experiments under various acoustic conditions are carried out to evaluate the three proposed methods. The comparison between them as well as with the baseline methods is presented.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processin

Parametric spatial audio processing utilising compact microphone arrays

This dissertation focuses on the development of novel parametric spatial audio techniques using compact microphone arrays. Compact arrays are of special interest since they can be adapted to fit in portable devices, opening the possibility of exploiting the potential of immersive spatial audio algorithms in our daily lives. The techniques developed in this thesis consider the use of signal processing algorithms adapted for human listeners, thus exploiting the capabilities and limitations of human spatial hearing. The findings of this research are in the following three areas of spatial audio processing: directional filtering, spatial audio reproduction, and direction of arrival estimation.  In directional filtering, two novel algorithms have been developed based on the cross-pattern coherence (CroPaC). The method essentially exploits the directional response of two different types of beamformers by using their cross-spectrum to estimate a soft masker. The soft masker provides a probability-like parameter that indicates whether there is sound present in specific directions. It is then used as a post-filter to provide further suppression of directionally distributed noise at the output of a beamformer. The performance of these algorithms represent a significant improvement over previous state-of-the-art methods.  In parametric spatial audio reproduction, an algorithm is developed for multi-channel loudspeaker and headphone rendering. Current limitations in spatial audio reproduction are related to high inter-channel coherence between the channels, which is common in signal-independent systems, or time-frequency artefacts in parametric systems. The developed algorithm focuses on solving these limitations by utilising two sets of beamformers. The first set of beamformers, namely analysis beamformers, is used to estimate a set of perceptually-relevant sound-field parameters, such as the separate channel energies, inter-channel time differences and inter-channel coherences of the target-output-setup signals. The directionality of the analysis beamformers is defined so that it follows that of typical loudspeaker panning functions and, for headphone reproduction, that of the head-related transfer functions (HRTFs). The directionality of the second set of high audio quality beamformers is then enhanced with the parametric information derived from the analysis beamformers. Listening tests confirm the perceptual benefit of such type of processing. In direction of arrival (DOA) estimation, histogram analysis of beamforming and active intensity based DOA estimators has been proposed. Numerical simulations and experiments with prototype and commercial microphone arrays show that the accuracy of DOA estimation is improved

Direct and Residual Subspace Decomposition of Spatial Room Impulse Responses

Psychoacoustic experiments have shown that directional properties of the direct sound, salient reflections, and the late reverberation of an acoustic room response can have a distinct influence on the auditory perception of a given room. Spatial room impulse responses (SRIRs) capture those properties and thus are used for direction-dependent room acoustic analysis and virtual acoustic rendering. This work proposes a subspace method that decomposes SRIRs into a direct part, which comprises the direct sound and the salient reflections, and a residual, to facilitate enhanced analysis and rendering methods by providing individual access to these components. The proposed method is based on the generalized singular value decomposition and interprets the residual as noise that is to be separated from the other components of the reverberation. Large generalized singular values are attributed to the direct part, which is then obtained as a low-rank approximation of the SRIR. By advancing from the end of the SRIR toward the beginning while iteratively updating the residual estimate, the method adapts to spatio-temporal variations of the residual. The method is evaluated using a spatio-spectral error measure and simulated SRIRs of different rooms, microphone arrays, and ratios of direct sound to residual energy. The proposed method creates lower errors than existing approaches in all tested scenarios, including a scenario with two simultaneous reflections. A case study with measured SRIRs shows the applicability of the method under real-world acoustic conditions. A reference implementation is provided

Effects of Coordinated Bilateral Hearing Aids and Auditory Training on Sound Localization

This thesis has two main objectives: 1) evaluating the benefits of the bilateral coordination of the hearing aid Digital Signal Processing (DSP) features by measuring and comparing the auditory performance with and without the activation of this coordination, and 2) evaluating the benefits of acclimatization and auditory training on such auditory performance and, determining whether receiving training in one aspect of auditory performance (sound localization) would generalize to an improvement in another aspect of auditory performance (speech intelligibility in noise), and to what extent. Two studies were performed. The first study evaluated the speech intelligibility in noise and horizontal sound localization abilities in HI listeners using hearing aids that apply bilateral coordination of WDRC. A significant improvement was noted in sound localization with bilateral coordination on when compared to off, while speech intelligibility in noise did not seem to be affected. The second study was an extension of the first study, with a suitable period for acclimatization provided and then the participants were divided into training and control groups. Only the training group received auditory training. The training group performance was significantly better than the control group performance in some conditions, in both the speech intelligibility and the localization tasks. The bilateral coordination did not have significant effects on the results of the second study. This work is among the early literature to investigate the impact of bilateral coordination in hearing aids on the users’ auditory performance. Also, this work is the first to demonstrate the effect of auditory training in sound localization on the speech intelligibility performance

Early adductive reasoning for blind signal separation

We demonstrate that explicit and systematic incorporation of abductive reasoning capabilities into algorithms for blind signal separation can yield significant performance improvements. Our formulated mechanisms apply to the output data of signal processing modules in order to conjecture the structure of time-frequency interactions between the signal components that are to be separated. The conjectured interactions are used to drive subsequent signal separation processes that are as a result less blind to the interacting signal components and, therefore, more effective. We refer to this type of process as early abductive reasoning (EAR); the “early” refers to the fact that in contrast to classical Artificial Intelligence paradigms, the reasoning process here is utilized before the signal processing transformations are completed. We have used our EAR approach to formulate a practical algorithm that is more effective in realistically noisy conditions than reference algorithms that are representative of the current state of the art in two-speaker pitch tracking. Our algorithm uses the Blackboard architecture from Artificial Intelligence to control EAR and advanced signal processing modules. The algorithm has been implemented in MATLAB and successfully tested on a database of 570 mixture signals representing simultaneous speakers in a variety of real-world, noisy environments. With 0 dB Target-to-Masking Ratio (TMR) and no noise, the Gross Error Rate (GER) for our algorithm is 5% in comparison to the best GER performance of 11% among the reference algorithms. In diffuse noisy environments (such as street or restaurant environments), we find that our algorithm on the average outperforms the best reference algorithm by 9.4%. With directional noise, our algorithm also outperforms the best reference algorithm by 29%. The extracted pitch tracks from our algorithm were also used to carry out comb filtering for separating the harmonics of the two speakers from each other and from the other sound sources in the environment. The separated signals were evaluated subjectively by a set of 20 listeners to be of reasonable quality