DESIGN AND EVALUATION OF HARMONIC SPEECH ENHANCEMENT AND BANDWIDTH EXTENSION

Improving the quality and intelligibility of speech signals continues to be an important topic in mobile communications and hearing aid applications. This thesis explored the possibilities of improving the quality of corrupted speech by cascading a log Minimum Mean Square Error (logMMSE) noise reduction system with a Harmonic Speech Enhancement (HSE) system. In HSE, an adaptive comb filter is deployed to harmonically filter the useful speech signal and suppress the noisy components to noise floor. A Bandwidth Extension (BWE) algorithm was applied to the enhanced speech for further improvements in speech quality. Performance of this algorithm combination was evaluated using objective speech quality metrics across a variety of noisy and reverberant environments. Results showed that the logMMSE and HSE combination enhanced the speech quality in any reverberant environment and in the presence of multi-talker babble. The objective improvements associated with the BWE were found to be minima

Blind dereverberation of speech from moving and stationary speakers using sequential Monte Carlo methods

Speech signals radiated in confined spaces are subject to reverberation due to reflections of surrounding walls and obstacles. Reverberation leads to severe degradation of speech intelligibility and can be prohibitive for applications where speech is digitally recorded, such as audio conferencing or hearing aids. Dereverberation of speech is therefore an important field in speech enhancement. Driven by consumer demand, blind speech dereverberation has become a popular field in the research community and has led to many interesting approaches in the literature. However, most existing methods are dictated by their underlying models and hence suffer from assumptions that constrain the approaches to specific subproblems of blind speech dereverberation. For example, many approaches limit the dereverberation to voiced speech sounds, leading to poor results for unvoiced speech. Few approaches tackle single-sensor blind speech dereverberation, and only a very limited subset allows for dereverberation of speech from moving speakers. Therefore, the aim of this dissertation is the development of a flexible and extendible framework for blind speech dereverberation accommodating different speech sound types, single- or multiple sensor as well as stationary and moving speakers. Bayesian methods benefit from – rather than being dictated by – appropriate model choices. Therefore, the problem of blind speech dereverberation is considered from a Bayesian perspective in this thesis. A generic sequential Monte Carlo approach accommodating a multitude of models for the speech production mechanism and room transfer function is consequently derived. In this approach both the anechoic source signal and reverberant channel are estimated using their optimal estimators by means of Rao-Blackwellisation of the state-space of unknown variables. The remaining model parameters are estimated using sequential importance resampling. The proposed approach is implemented for two different speech production models for stationary speakers, demonstrating substantial reduction in reverberation for both unvoiced and voiced speech sounds. Furthermore, the channel model is extended to facilitate blind dereverberation of speech from moving speakers. Due to the structure of measurement model, single- as well as multi-microphone processing is facilitated, accommodating physically constrained scenarios where only a single sensor can be used as well as allowing for the exploitation of spatial diversity in scenarios where the physical size of microphone arrays is of no concern. This dissertation is concluded with a survey of possible directions for future research, including the use of switching Markov source models, joint target tracking and enhancement, as well as an extension to subband processing for improved computational efficiency

Single- and multi-microphone speech dereverberation using spectral enhancement

In speech communication systems, such as voice-controlled systems, hands-free mobile telephones, and hearing aids, the received microphone signals are degraded by room reverberation, background noise, and other interferences. This signal degradation may lead to total unintelligibility of the speech and decreases the performance of automatic speech recognition systems. In the context of this work reverberation is the process of multi-path propagation of an acoustic sound from its source to one or more microphones. The received microphone signal generally consists of a direct sound, reflections that arrive shortly after the direct sound (commonly called early reverberation), and reflections that arrive after the early reverberation (commonly called late reverberation). Reverberant speech can be described as sounding distant with noticeable echo and colouration. These detrimental perceptual effects are primarily caused by late reverberation, and generally increase with increasing distance between the source and microphone. Conversely, early reverberations tend to improve the intelligibility of speech. In combination with the direct sound it is sometimes referred to as the early speech component. Reduction of the detrimental effects of reflections is evidently of considerable practical importance, and is the focus of this dissertation. More specifically the dissertation deals with dereverberation techniques, i.e., signal processing techniques to reduce the detrimental effects of reflections. In the dissertation, novel single- and multimicrophone speech dereverberation algorithms are developed that aim at the suppression of late reverberation, i.e., at estimation of the early speech component. This is done via so-called spectral enhancement techniques that require a specific measure of the late reverberant signal. This measure, called spectral variance, can be estimated directly from the received (possibly noisy) reverberant signal(s) using a statistical reverberation model and a limited amount of a priori knowledge about the acoustic channel(s) between the source and the microphone(s). In our work an existing single-channel statistical reverberation model serves as a starting point. The model is characterized by one parameter that depends on the acoustic characteristics of the environment. We show that the spectral variance estimator that is based on this model, can only be used when the source-microphone distance is larger than the so-called critical distance. This is, crudely speaking, the distance where the direct sound power is equal to the total reflective power. A generalization of the statistical reverberation model in which the direct sound is incorporated is developed. This model requires one additional parameter that is related to the ratio between the direct sound energy and the sound energy of all reflections. The generalized model is used to derive a novel spectral variance estimator. When the novel estimator is used for dereverberation rather than the existing estimator, and the source-microphone distance is smaller than the critical distance, the dereverberation performance is significantly increased. Single-microphone systems only exploit the temporal and spectral diversity of the received signal. Reverberation, of course, also induces spatial diversity. To additionally exploit this diversity, multiple microphones must be used, and their outputs must be combined by a suitable spatial processor such as the so-called delay and sum beamformer. It is not a priori evident whether spectral enhancement is best done before or after the spatial processor. For this reason we investigate both possibilities, as well as a merge of the spatial processor and the spectral enhancement technique. An advantage of the latter option is that the spectral variance estimator can be further improved. Our experiments show that the use of multiple microphones affords a significant improvement of the perceptual speech quality. The applicability of the theory developed in this dissertation is demonstrated using a hands-free communication system. Since hands-free systems are often used in a noisy and reverberant environment, the received microphone signal does not only contain the desired signal but also interferences such as room reverberation that is caused by the desired source, background noise, and a far-end echo signal that results from a sound that is produced by the loudspeaker. Usually an acoustic echo canceller is used to cancel the far-end echo. Additionally a post-processor is used to suppress background noise and residual echo, i.e., echo which could not be cancelled by the echo canceller. In this work a novel structure and post-processor for an acoustic echo canceller are developed. The post-processor suppresses late reverberation caused by the desired source, residual echo, and background noise. The late reverberation and late residual echo are estimated using the generalized statistical reverberation model. Experimental results convincingly demonstrate the benefits of the proposed system for suppressing late reverberation, residual echo and background noise. The proposed structure and post-processor have a low computational complexity, a highly modular structure, can be seamlessly integrated into existing hands-free communication systems, and affords a significant increase of the listening comfort and speech intelligibility

Speech Dereverberation Based on Multi-Channel Linear Prediction

Room reverberation can severely degrade the auditory quality and intelligibility of the speech signals received by distant microphones in an enclosed environment. In recent years, various dereverberation algorithms have been developed to tackle this problem, such as beamforming and inverse filtering of the room transfer function. However, this kind of methods relies heavily on the precise estimation of either the direction of arrival (DOA) or room acoustic characteristics. Thus, their performance is very much limited. A more promising category of dereverberation algorithms has been developed based on multi-channel linear predictor (MCLP). This idea was first proposed in time domain where speech signal is highly correlated in a short period of time. To ensure a good suppression of the reverberation, the prediction filter length is required to be longer than the reverberation time. As a result, the complexity of this algorithm is often unacceptable because of large covariance matrix calculation. To overcome this disadvantage, this thesis focuses on the MCLP dereverberation methods performed in the short-time Fourier transform (STFT) domain. Recently, the weighted prediction error (WPE) algorithm has been developed and widely applied to speech dereverberation. In WPE algorithm, MCLP is used in the STFT domain to estimate the late reverberation components from previous frames of the reverberant speech. The enhanced speech is obtained by subtracting the late reverberation from the reverberant speech. Each STFT coefficient is assumed to be independent and obeys Gaussian distribution. A maximum likelihood (ML) problem is formulated in each frequency bin to calculate the predictor coefficients. In this thesis, the original WPE algorithm is improved in two aspects. First, two advanced statistical models, generalized Gaussian distribution (GGD) and Laplacian distribution, are employed instead of the classic Gaussian distribution. Both of them are shown to give better modeling of the histogram of the clean speech. Second, we focus on improving the estimation of the variances of the STFT coefficients of the desired signal. In the original WPE algorithm, the variances are estimated in each frequency bin independently without considering the cross-frequency correlation. Thus, we integrate the nonnegative matrix factorization (NMF) into the WPE algorithm to refine the estimation of the variances and hence obtain a better dereverberation performance. Another category of MCLP based dereverberation algorithm has been proposed in literature by exploiting the sparsity of the STFT coefficients of the desired signal for calculating the predictor coefficients. In this thesis, we also investigate an efficient algorithm based on the maximization of the group sparsity of desired signal using mixed norms. Inspired by the idea of sparse linear predictor (SLP), we propose to include a sparse constraint for the predictor coefficients in order to further improve the dereverberation performance. A weighting parameter is also introduced to achieve a trade-off between the sparsity of the desired signal and the predictor coefficients. Computer simulation of the proposed dereverberation algorithms is conducted. Our experimental results show that the proposed algorithms can significantly improve the quality of reverberant speech signal under different reverberation times. Subjective evaluation also gives a more intuitive demonstration of the enhanced speech intelligibility. Performance comparison also shows that our algorithms outperform some of the state-of-the-art dereverberation techniques

Estimation of room acoustic parameters: the ACE challenge

Reverberation Time (T60) and Direct-to-Reverberant Ratio (DRR) are important parameters which together can characterize sound captured by microphones in non-anechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T60 and DRR can be estimated directly from the Acoustic Impulse Response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The Acoustic Characterization of Environments (ACE) Challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE Challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful

Coding Strategies for Cochlear Implants Under Adverse Environments

Cochlear implants are electronic prosthetic devices that restores partial hearing in patients with severe to profound hearing loss. Although most coding strategies have significantly improved the perception of speech in quite listening conditions, there remains limitations on speech perception under adverse environments such as in background noise, reverberation and band-limited channels, and we propose strategies that improve the intelligibility of speech transmitted over the telephone networks, reverberated speech and speech in the presence of background noise. For telephone processed speech, we propose to examine the effects of adding low-frequency and high- frequency information to the band-limited telephone speech. Four listening conditions were designed to simulate the receiving frequency characteristics of telephone handsets. Results indicated improvement in cochlear implant and bimodal listening when telephone speech was augmented with high frequency information and therefore this study provides support for design of algorithms to extend the bandwidth towards higher frequencies. The results also indicated added benefit from hearing aids for bimodal listeners in all four types of listening conditions. Speech understanding in acoustically reverberant environments is always a difficult task for hearing impaired listeners. Reverberated sounds consists of direct sound, early reflections and late reflections. Late reflections are known to be detrimental to speech intelligibility. In this study, we propose a reverberation suppression strategy based on spectral subtraction to suppress the reverberant energies from late reflections. Results from listening tests for two reverberant conditions (RT60 = 0.3s and 1.0s) indicated significant improvement when stimuli was processed with SS strategy. The proposed strategy operates with little to no prior information on the signal and the room characteristics and therefore, can potentially be implemented in real-time CI speech processors. For speech in background noise, we propose a mechanism underlying the contribution of harmonics to the benefit of electroacoustic stimulations in cochlear implants. The proposed strategy is based on harmonic modeling and uses synthesis driven approach to synthesize the harmonics in voiced segments of speech. Based on objective measures, results indicated improvement in speech quality. This study warrants further work into development of algorithms to regenerate harmonics of voiced segments in the presence of noise

Speech Enhancement Exploiting the Source-Filter Model

Imagining everyday life without mobile telephony is nowadays hardly possible. Calls are being made in every thinkable situation and environment. Hence, the microphone will not only pick up the user’s speech but also sound from the surroundings which is likely to impede the understanding of the conversational partner. Modern speech enhancement systems are able to mitigate such effects and most users are not even aware of their existence. In this thesis the development of a modern single-channel speech enhancement approach is presented, which uses the divide and conquer principle to combat environmental noise in microphone signals. Though initially motivated by mobile telephony applications, this approach can be applied whenever speech is to be retrieved from a corrupted signal. The approach uses the so-called source-filter model to divide the problem into two subproblems which are then subsequently conquered by enhancing the source (the excitation signal) and the filter (the spectral envelope) separately. Both enhanced signals are then used to denoise the corrupted signal. The estimation of spectral envelopes has quite some history and some approaches already exist for speech enhancement. However, they typically neglect the excitation signal which leads to the inability of enhancing the fine structure properly. Both individual enhancement approaches exploit benefits of the cepstral domain which offers, e.g., advantageous mathematical properties and straightforward synthesis of excitation-like signals. We investigate traditional model-based schemes like Gaussian mixture models (GMMs), classical signal processing-based, as well as modern deep neural network (DNN)-based approaches in this thesis. The enhanced signals are not used directly to enhance the corrupted signal (e.g., to synthesize a clean speech signal) but as so-called a priori signal-to-noise ratio (SNR) estimate in a traditional statistical speech enhancement system. Such a traditional system consists of a noise power estimator, an a priori SNR estimator, and a spectral weighting rule that is usually driven by the results of the aforementioned estimators and subsequently employed to retrieve the clean speech estimate from the noisy observation. As a result the new approach obtains significantly higher noise attenuation compared to current state-of-the-art systems while maintaining a quite comparable speech component quality and speech intelligibility. In consequence, the overall quality of the enhanced speech signal turns out to be superior as compared to state-of-the-art speech ehnahcement approaches.Mobiltelefonie ist aus dem heutigen Leben nicht mehr wegzudenken. Telefonate werden in beliebigen Situationen an beliebigen Orten geführt und dabei nimmt das Mikrofon nicht nur die Sprache des Nutzers auf, sondern auch die Umgebungsgeräusche, welche das Verständnis des Gesprächspartners stark beeinflussen können. Moderne Systeme können durch Sprachverbesserungsalgorithmen solchen Effekten entgegenwirken, dabei ist vielen Nutzern nicht einmal bewusst, dass diese Algorithmen existieren. In dieser Arbeit wird die Entwicklung eines einkanaligen Sprachverbesserungssystems vorgestellt. Der Ansatz setzt auf das Teile-und-herrsche-Verfahren, um störende Umgebungsgeräusche aus Mikrofonsignalen herauszufiltern. Dieses Verfahren kann für sämtliche Fälle angewendet werden, in denen Sprache aus verrauschten Signalen extrahiert werden soll. Der Ansatz nutzt das Quelle-Filter-Modell, um das ursprüngliche Problem in zwei Unterprobleme aufzuteilen, die anschließend gelöst werden, indem die Quelle (das Anregungssignal) und das Filter (die spektrale Einhüllende) separat verbessert werden. Die verbesserten Signale werden gemeinsam genutzt, um das gestörte Mikrofonsignal zu entrauschen. Die Schätzung von spektralen Einhüllenden wurde bereits in der Vergangenheit erforscht und zum Teil auch für die Sprachverbesserung angewandt. Typischerweise wird dabei jedoch das Anregungssignal vernachlässigt, so dass die spektrale Feinstruktur des Mikrofonsignals nicht verbessert werden kann. Beide Ansätze nutzen jeweils die Eigenschaften der cepstralen Domäne, die unter anderem vorteilhafte mathematische Eigenschaften mit sich bringen, sowie die Möglichkeit, Prototypen eines Anregungssignals zu erzeugen. Wir untersuchen modellbasierte Ansätze, wie z.B. Gaußsche Mischmodelle, klassische signalverarbeitungsbasierte Lösungen und auch moderne tiefe neuronale Netzwerke in dieser Arbeit. Die so verbesserten Signale werden nicht direkt zur Sprachsignalverbesserung genutzt (z.B. Sprachsynthese), sondern als sogenannter A-priori-Signal-zu-Rauschleistungs-Schätzwert in einem traditionellen statistischen Sprachverbesserungssystem. Dieses besteht aus einem Störleistungs-Schätzer, einem A-priori-Signal-zu-Rauschleistungs-Schätzer und einer spektralen Gewichtungsregel, die üblicherweise mit Hilfe der Ergebnisse der beiden Schätzer berechnet wird. Schließlich wird eine Schätzung des sauberen Sprachsignals aus der Mikrofonaufnahme gewonnen. Der neue Ansatz bietet eine signifikant höhere Dämpfung des Störgeräuschs als der bisherige Stand der Technik. Dabei wird eine vergleichbare Qualität der Sprachkomponente und der Sprachverständlichkeit gewährleistet. Somit konnte die Gesamtqualität des verbesserten Sprachsignals gegenüber dem Stand der Technik erhöht werden