Blind Signal Separation for Digital Communication Data

to appear in EURASIP E-reference in Signal Processing, invited paper.International audienceBlind source separation, often called independent component analysis , is a main field of research in signal processing since the eightees. It consists in retrieving the components, up to certain indeterminacies, of a mixture involving statistically independent signals. Solid theoretical results are known; besides, they have given rise to performent algorithms. There are numerous applications of blind source separation. In this contribution, we particularize the separation of telecommunication sources. In this context, the sources stem from telecommunication devices transmitting at the same time in a given band of frequencies. The received data is a mixed version of all these sources. The aim of the receiver is to isolate (separate) the different contributions prior to estimating the unknown parameters associated with a transmitter. The context of telecommunication signals has the particularity that the sources are not stationary but cyclo-stationary. Now, in general, the standard methods of blind source separation assume the stationarity of the sources. In this contribution , we hence make a survey of the well-known methods and show how the results extend to cyclo-stationary sources

Single-Channel Signal Separation Using Spectral Basis Correlation with Sparse Nonnegative Tensor Factorization

A novel approach for solving the single-channel signal separation is presented the proposed sparse nonnegative tensor factorization under the framework of maximum a posteriori probability and adaptively fine-tuned using the hierarchical Bayesian approach with a new mixing mixture model. The mixing mixture is an analogy of a stereo signal concept given by one real and the other virtual microphones. An “imitated-stereo” mixture model is thus developed by weighting and time-shifting the original single-channel mixture. This leads to an artificial mixing system of dual channels which gives rise to a new form of spectral basis correlation diversity of the sources. Underlying all factorization algorithms is the principal difficulty in estimating the adequate number of latent components for each signal. This paper addresses these issues by developing a framework for pruning unnecessary components and incorporating a modified multivariate rectified Gaussian prior information into the spectral basis features. The parameters of the imitated-stereo model are estimated via the proposed sparse nonnegative tensor factorization with Itakura–Saito divergence. In addition, the separability conditions of the proposed mixture model are derived and demonstrated that the proposed method can separate real-time captured mixtures. Experimental testing on real audio sources has been conducted to verify the capability of the proposed method

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Temporal and time-frequency correlation-based blind source separation methods. Part I : Determined and underdetermined linear instantaneous mixtures

We propose two types of correlation-based blind source separation (BSS) methods, i.e. a time-domain approach and extensions which use time-frequency (TF) signal representations and thus apply to much more general conditions. Our basic TF methods only require each source to be isolated in a tiny TF area, i.e. they set very limited constraints on the source sparsity and overlap, unlike various previously reported TF-BSS methods. Our approaches consist in identifying the columns of the (scaled permuted) mixing matrix in TF areas where these methods detect that a source is isolated. Both the detection and identification stages of these approaches use local correlation parameters of the TF transforms of the observed signals. Two such Linear Instantaneous TIme-Frequency CORRelation-based BSS methods are proposed, using Centered or Non-Centered TF transforms. These methods, which are resp. called LI-TIFCORR-C and LI-TIFCORR-NC, are especially suited to non-stationary sources. We derive their performance from many tests performed with mixtures of speech signals. This demonstrates that their output SIRs have a low sensitivity to the values of their TF parameters and are quite high, i.e. typically 60 to 80 dB, while the SIRs of all tested classical methods range about from 0 to 40 dB. We also extend these approaches to achieve partial BSS for underdetermined mixtures and to operate when some sources are not isolated in any TF area

Clustering Inverse Beamforming and multi-domain acoustic imaging approaches for vehicles NVH

Il rumore percepito all’interno della cabina di un veicolo è un aspetto molto rilevante nella valutazione della sua qualità complessiva. Metodi sperimentali di acoustic imaging, quali beamforming e olografia acustica, sono usati per identificare le principali sorgenti che contribuiscono alla rumorosità percepita all’interno del veicolo. L’obiettivo della tesi proposta è di fornire strumenti per effettuare dettagliate analisi quantitative tramite tali tecniche, ad oggi relegate alle fasi di studio preliminare, proponendo un approccio modulare che si avvale di analisi dei fenomeni vibro-acustici nel dominio della frequenza, del tempo e dell’angolo di rotazione degli elementi rotanti tipicamente presenti in un veicolo. Ciò permette di ridurre tempi e costi della progettazione, garantendo, al contempo, una maggiore qualità del pacchetto vibro-acustico. L’innovativo paradigma proposto prevede l’uso combinato di algoritmi di pre- e post- processing con tecniche inverse di acoustic imaging per lo studio di rilevanti problematiche quali l’identificazione di sorgenti sonore esterne o interne all’abitacolo e del rumore prodotto da dispositivi rotanti. Principale elemento innovativo della tesi è la tecnica denominata Clustering Inverse Beamforming. Essa si basa su un approccio statistico che permette di incrementare l’accuratezza (range dinamico, localizzazione e quantificazione) di una immagine acustica tramite la combinazione di soluzioni, del medesimo problema inverso, ottenute considerando diversi sotto-campioni dell’informazione sperimentale disponibile, variando, in questo modo, in maniera casuale la sua formulazione matematica. Tale procedimento garantisce la ricostruzione nel dominio della frequenza e del tempo delle sorgenti sonore identificate. Un metodo innovativo è stato inoltre proposto per la ricostruzione, ove necessario, di sorgenti sonore nel dominio dell’angolo. I metodi proposti sono stati supportati da argomentazioni teoriche e validazioni sperimentali su scala accademica e industriale.The interior sound perceived in vehicle cabins is a very important attribute for the user. Experimental acoustic imaging methods such as beamforming and Near-field Acoustic Holography are used in vehicles noise and vibration studies because they are capable of identifying the noise sources contributing to the overall noise perceived inside the cabin. However these techniques are often relegated to the troubleshooting phase, thus requiring additional experiments for more detailed NVH analyses. It is therefore desirable that such methods evolve towards more refined solutions capable of providing a larger and more detailed information. This thesis proposes a modular and multi-domain approach involving direct and inverse acoustic imaging techniques for providing quantitative and accurate results in frequency, time and angle domain, thus targeting three relevant types of problems in vehicles NVH: identification of exterior sources affecting interior noise, interior noise source identification, analysis of noise sources produced by rotating machines. The core finding of this thesis is represented by a novel inverse acoustic imaging method named Clustering Inverse Beamforming (CIB). The method grounds on a statistical processing based on an Equivalent Source Method formulation. In this way, an accurate localization, a reliable ranking of the identified sources in frequency domain and their separation into uncorrelated phenomena is obtained. CIB is also exploited in this work for allowing the reconstruction of the time evolution of the sources sought. Finally a methodology for decomposing the acoustic image of the sound field generated by a rotating machine as a function of the angular evolution of the machine shaft is proposed. This set of findings aims at contributing to the advent of a new paradigm of acoustic imaging applications in vehicles NVH, supporting all the stages of the vehicle design with time-saving and cost-efficient experimental techniques. The proposed innovative approaches are validated on several simulated and real experiments