Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks

Sampling rate offsets (SROs) between devices in a heterogeneous wireless acoustic sensor network (WASN) can hinder the ability of distributed adaptive algorithms to perform as intended when they rely on coherent signal processing. In this paper, we present an SRO estimation and compensation method to allow the deployment of the distributed adaptive node-specific signal estimation (DANSE) algorithm in WASNs composed of asynchronous devices. The signals available at each node are first utilised in a coherence-drift-based method to blindly estimate SROs which are then compensated for via phase shifts in the frequency domain. A modification of the weighted overlap-add (WOLA) implementation of DANSE is introduced to account for SRO-induced full-sample drifts, permitting per-sample signal transmission via an approximation of the WOLA process as a time-domain convolution. The performance of the proposed algorithm is evaluated in the context of distributed noise reduction for the estimation of a target speech signal in an asynchronous WASN.Comment: 9 pages, 6 figure

DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays

Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.Comment: Submitted to TASL

DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays

Submitted to ICASSP2020International audienceMultichannel processing is widely used for speech enhancement but several limitations appear when trying to deploy these solutions to the real-world. Distributed sensor arrays that consider several devices with a few microphones is a viable alternative that allows for exploiting the multiple devices equipped with microphones that we are using in our everyday life. In this context, we propose to extend the distributed adaptive node-specific signal estimation approach to a neural networks framework. At each node, a local filtering is performed to send one signal to the other nodes where a mask is estimated by a neural network in order to compute a global multi-channel Wiener filter. In an array of two nodes, we show that this additional signal can be efficiently taken into account to predict the masks and leads to better speech enhancement performances than when the mask estimation relies only on the local signals

Distributed canonical correlation analysis in wireless sensor networks with application to distributed blind source separation

status: publishe

Informed source extraction from a mixture of sources exploiting second order temporal structure

Extracting a specific signal from among man

Speech enhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle

A method is proposed for performing speech enhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle (UAV). The ego-noise reference signals are captured with microphones located near the UAV's propellers and used in the prior knowledge multichannel Wiener filter (PK-MWF) to obtain the speech correlation matrix estimate. Speech presence probability (SPP) can be estimated for detecting speech activity from an external microphone near the speech source, providing a performance benchmark, or from one of the embedded microphones, assuming a more realistic scenario. Experimental measurements are performed in a semi-anechoic chamber, with a UAV mounted on a stand and a loudspeaker playing a speech signal, while setting three distinct and fixed propeller rotation speeds, resulting in three different signal-to-noise ratios (SNRs). The recordings obtained and made available online are used to compare the proposed method to the use of the standard multichannel Wiener filter (MWF) estimated with and without the propellers' microphones being used in its formulation. Results show that compared to those, the use of PK-MWF achieves higher levels of improvement in speech intelligibility and quality, measured by STOI and PESQ, while the SNR improvement is similar

Robust Distributed Multi-Source Detection and Labeling in Wireless Acoustic Sensor Networks

The growing demand in complex signal processing methods associated with low-energy large scale wireless acoustic sensor networks (WASNs) urges the shift to a new information and communication technologies (ICT) paradigm. The emerging research perception aspires for an appealing wireless network communication where multiple heterogeneous devices with different interests can cooperate in various signal processing tasks (MDMT). Contributions in this doctoral thesis focus on distributed multi-source detection and labeling applied to audio enhancement scenarios pursuing an MDMT fashioned node-specific source-of-interest signal enhancement in WASNs. In fact, an accurate detection and labeling is a pre-requisite to pursue the MDMT paradigm where nodes in the WASN communicate effectively their sources-of-interest and, therefore, multiple signal processing tasks can be enhanced via cooperation. First, a novel framework based on a dominant source model in distributed WASNs for resolving the activity detection of multiple speech sources in a reverberant and noisy environment is introduced. A preliminary rank-one multiplicative non-negative independent component analysis (M-NICA) for unique dominant energy source extraction given associated node clusters is presented. Partitional algorithms that minimize the within-cluster mean absolute deviation (MAD) and weighted MAD objectives are proposed to determine the cluster membership of the unmixed energies, and thus establish a source specific voice activity recognition. In a second study, improving the energy signal separation to alleviate the multiple source activity discrimination task is targeted. Sparsity inducing penalties are enforced on iterative rank-one singular value decomposition layers to extract sparse right rotations. Then, sparse non-negative blind energy separation is realized using multiplicative updates. Hence, the multiple source detection problem is converted into a sparse non-negative source energy decorrelation. Sparsity tunes the supposedly non-active energy signatures to exactly zero-valued energies so that it is easier to identify active energies and an activity detector can be constructed in a straightforward manner. In a centralized scenario, the activity decision is controlled by a fusion center that delivers the binary source activity detection for every participating energy source. This strategy gives precise detection results for small source numbers. With a growing number of interfering sources, the distributed detection approach is more promising. Conjointly, a robust distributed energy separation algorithm for multiple competing sources is proposed. A robust and regularized $t_{\nu}M$-estimation of the covariance matrix of the mixed energies is employed. This approach yields a simple activity decision using only the robustly unmixed energy signatures of the sources in the WASN. The performance of the robust activity detector is validated with a distributed adaptive node-specific signal estimation method for speech enhancement. The latter enhances the quality and intelligibility of the signal while exploiting the accurately estimated multi-source voice decision patterns. In contrast to the original M-NICA for source separation, the extracted binary activity patterns with the robust energy separation significantly improve the node-specific signal estimation. Due to the increased computational complexity caused by the additional step of energy signal separation, a new approach to solving the detection question of multi-device multi-source networks is presented. Stability selection for iterative extraction of robust right singular vectors is considered. The sub-sampling selection technique provides transparency in properly choosing the regularization variable in the Lasso optimization problem. In this way, the strongest sparse right singular vectors using a robust $\ell_1$-norm and stability selection are the set of basis vectors that describe the input data efficiently. Active/non-active source classification is achieved based on a robust Mahalanobis classifier. For this, a robust $M$-estimator of the covariance matrix in the Mahalanobis distance is utilized. Extensive evaluation in centralized and distributed settings is performed to assess the effectiveness of the proposed approach. Thus, overcoming the computationally demanding source separation scheme is possible via exploiting robust stability selection for sparse multi-energy feature extraction. With respect to the labeling problem of various sources in a WASN, a robust approach is introduced that exploits the direction-of-arrival of the impinging source signals. A short-time Fourier transform-based subspace method estimates the angles of locally stationary wide band signals using a uniform linear array. The median of angles estimated at every frequency bin is utilized to obtain the overall angle for each participating source. The features, in this case, exploit the similarity across devices in the particular frequency bins that produce reliable direction-of-arrival estimates for each source. Reliability is defined with respect to the median across frequencies. All source-specific frequency bands that contribute to correct estimated angles are selected. A feature vector is formed for every source at each device by storing the frequency bin indices that lie within the upper and lower interval of the median absolute deviation scale of the estimated angle. Labeling is accomplished by a distributed clustering of the extracted angle-based feature vectors using consensus averaging

Contributions to speech processing and ambient sound analysis

We are constantly surrounded by sounds that we continuously exploit to adapt our actions to situations we are facing. Some of the sounds like speech can have a particular structure from which we can infer some information, explicit or not. This is one reason why speech is possibly that is the most intuitive way to communicate between humans. Within the last decade, there has been significant progress in the domain of speech andaudio processing and in particular in the domain of machine learning applied to speech and audio processing. Thanks to these progresses, speech has become a central element in many human to human distant communication tools as well as in human to machine communication systems. These solutions work pretty well on clean speech or under controlled condition. However, in scenarios that involve the presence of acoustic perturbation such as noise or reverberation systems performance tends to degrade severely. In this thesis we focus on processing speech and its environments from an audio perspective. The algorithms proposed here are relying on a variety of solutions from signal processing based approaches to data-driven solutions based on supervised matrix factorization or deep neural networks. We propose solutions to problems ranging from speech recognition, to speech enhancement or ambient sound analysis. The target is to offer a panorama of the different aspects that could improve a speech processing algorithm working in a real environments. We start by describing automatic speech recognition as a potential end application and progressively unravel the limitations and the proposed solutions ending-up to the more general ambient sound analysis.Nous sommes constamment entourés de sons que nous exploitons pour adapter nos actions aux situations auxquelles nous sommes confrontés. Certains sons comme la parole peuvent avoir une structure particulière à partir de laquelle nous pouvons déduire des informations, explicites ou non. C’est l’une des raisons pour lesquelles la parole est peut-être le moyen le plus intuitif de communiquer entre humains. Au cours de la décennie écoulée, des progrès significatifs ont été réalisés dans le domaine du traitement de la parole et du son et en particulier dans le domaine de l’apprentissage automatique appliqué au traitement de la parole et du son. Grâce à ces progrès, la parole est devenue un élément central de nombreux outils de communication à distance d’humain à humain ainsi que dans les systèmes de communication humain-machine. Ces solutions fonctionnent bien sur un signal de parole propre ou dans des conditions contrôlées. Cependant, dans les scénarios qui impliquent la présence de perturbations acoustiques telles que du bruit ou de la réverbération les performances peuvent avoir tendance à se dégrader gravement. Dans cette HDR, nous nous concentrons sur le traitement de la parole et de son environnement d’un point de vue audio. Les algorithmes proposés ici reposent sur une variété de solutions allant des approches basées sur le traitement du signal aux solutions orientées données à base de factorisation matricielle supervisée ou de réseaux de neurones profonds. Nous proposons des solutions à des problèmes allant de la reconnaissance vocale au rehaussement de la parole ou à l’analyse des sons ambiants. L’objectif est d’offrir un panorama des différents aspects qui pourraient être améliorer un algorithme de traitement de la parole fonctionnant dans un environnement réel. Nous commençons par décrire la reconnaissance automatique de la parole comme une application finale potentielle et analysons progressivement les limites et les solutions proposées aboutissant à l’analyse plus générale des sons ambiants

Array processing techniques for direction of arrival estimation, communications, and localization in vehicular and wireless sensor networks

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Técnicas de processamentos de sinais para comunicações sem fio tem sido um tópico de interesse para pesquisas há mais de três décadas. De acordo com o padrão Release 9 desenvolvido pelo consorcio 3rd Generation Partnership Project (3GPP) sistemas utilizando múltiplas antenas foram adotados na quarta geração (4G) dos sistemas de comunicação sem fio, também conhecida em inglês como Long Term Evolution (LTE). Para a quinta geração (5G) dos sistemas de comunicação sem fio centenas de antenas devem ser incorporadas aos equipamentos, na arquitetura conhecida em inglês como massive multi-user Multiple Input Multiple Output (MIMO). A presença de múltiplas antenas provê benefícios como o ganho do arranjo, ganho de diversidade, ganho espacial e redução de interferência. Além disso, arranjos de antenas possibilitam a filtragem espacial e a estimação de parâmetros, ambos podem ser usados para se resolver problemas que antes não eram vistos pelo prisma de processamento de sinais. O objetivo dessa tese é superar a lacuna entre a teoria de processamento de sinais e as aplicações da mesma em problemas reais. Tradicionalmente, técnicas de processamento de sinais assumem a existência de um arranjo de antenas ideal. Portanto, para que tais técnicas sejam exploradas em aplicações reais, um conjunto robusto de métodos para interpolação do arranjo é fundamental. Estes métodos são desenvolvidos nesta tese. Além disso problemas no campo de redes de sensores e redes veiculares são tratados nesta tese utilizando-se uma perspectiva de processamento de sinais. Nessa tesa métodos inovadores de interpolação de arranjos são apresentados e sua performance é testada utilizando-se cenários reais. Conceitos de processamento de sinais são implementados no contexto de redes de sensores. Esses conceitos possibilitam um nível de sincronização suficiente para a aplicação de sistemas de múltiplas antenas distribuídos, o que resulta em uma rede com maior vida útil e melhor performance. Métodos de processamento de sinais em arranjos são propostos para resolver o problema de localização baseada em sinais de rádio em redes veiculares, com aplicações em segurança de estradas e proteção de pedestres. Esta tese foi escrita em língua inglesa, um sumário em língua portuguesa é apresentado ao final da mesma.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Array signal processing in wireless communication has been a topic of interest in research for over three decades. In the fourth generation (4G) of the wireless communication systems, also known as Long Term Evolution (LTE), multi antenna systems have been adopted according to the Release 9 of the 3rd Generation Partnership Project (3GPP). For the fifth generation (5G) of the wireless communication systems, hundreds of antennas should be incorporated to the devices in a massive multi-user Multiple Input Multiple Output (MIMO) architecture. The presence of multiple antennas provides array gain, diversity gain, spatial gain, and interference reduction. Furthermore, arrays enable spatial filtering and parameter estimation, which can be used to help solve problems that could not previously be addressed from a signal processing perspective. The aim of this thesis is to bridge some gaps between signal processing theory and real world applications. Array processing techniques traditionally assume an ideal array. Therefore, in order to exploit such techniques, a robust set of methods for array interpolation are fundamental and are developed in this work. Problems in the field of wireless sensor networks and vehicular networks are also addressed from an array signal processing perspective. In this dissertation, novel methods for array interpolation are presented and their performance in real world scenarios is evaluated. Signal processing concepts are implemented in the context of a wireless sensor network. These concepts provide a level of synchronization sufficient for distributed multi antenna communication to be applied, resulting in improved lifetime and improved overall network behaviour. Array signal processing methods are proposed to solve the problem of radio based localization in vehicular network scenarios with applications in road safety and pedestrian protection