Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acoustic from the unknown competing speech sources relying on localization of the early images of the speakers by sparse approximation of the spatial spectra of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech recovery and recognition.Comment: 31 page

Direction of Arrival Estimation in the Spherical Harmonic Domain using Subspace Pseudo-Intensity Vectors

Direction of Arrival (DOA) estimation is a fundamental problem in acoustic signal processing. It is used in a diverse range of applications, including spatial filtering, speech dereverberation, source separation and diarization. Intensity vector-based DOA estimation is attractive, especially for spherical sensor arrays, because it is computationally efficient. Two such methods are presented which operate on a spherical harmonic decomposition of a sound field observed using a spherical microphone array. The first uses Pseudo-Intensity Vectors (PIVs) and works well in acoustic environments where only one sound source is active at any time. The second uses Subspace Pseudo-Intensity Vectors (SSPIVs) and is targeted at environments where multiple simultaneous sources and significant levels of reverberation make the problem more challenging. Analytical models are used to quantify the effects of an interfering source, diffuse noise and sensor noise on PIVs and SSPIVs. The accuracy of DOA estimation using PIVs and SSPIVs is compared against the state-of-the-art in simulations including realistic reverberation and noise for single and multiple, stationary and moving sources. Finally, robust performance of the proposed methods is demonstrated using speech recordings in real acoustic environments

An Online Solution for Localisation, Tracking and Separation of Moving Speech Sources

The problem of separating a time varying number of speech sources in a room is difficult to solve. The challenge lies in estimating the number and the location of these speech sources. Furthermore, the tracked speech sources need to be separated. This thesis proposes a solution which utilises the Random Finite Set approach to estimate the number and location of these speech sources and subsequently separate the speech source mixture via time frequency masking

Application of sound source separation methods to advanced spatial audio systems

This thesis is related to the field of Sound Source Separation (SSS). It addresses the development and evaluation of these techniques for their application in the resynthesis of high-realism sound scenes by means of Wave Field Synthesis (WFS). Because the vast majority of audio recordings are preserved in twochannel stereo format, special up-converters are required to use advanced spatial audio reproduction formats, such as WFS. This is due to the fact that WFS needs the original source signals to be available, in order to accurately synthesize the acoustic field inside an extended listening area. Thus, an object-based mixing is required. Source separation problems in digital signal processing are those in which several signals have been mixed together and the objective is to find out what the original signals were. Therefore, SSS algorithms can be applied to existing two-channel mixtures to extract the different objects that compose the stereo scene. Unfortunately, most stereo mixtures are underdetermined, i.e., there are more sound sources than audio channels. This condition makes the SSS problem especially difficult and stronger assumptions have to be taken, often related to the sparsity of the sources under some signal transformation. This thesis is focused on the application of SSS techniques to the spatial sound reproduction field. As a result, its contributions can be categorized within these two areas. First, two underdetermined SSS methods are proposed to deal efficiently with the separation of stereo sound mixtures. These techniques are based on a multi-level thresholding segmentation approach, which enables to perform a fast and unsupervised separation of sound sources in the time-frequency domain. Although both techniques rely on the same clustering type, the features considered by each of them are related to different localization cues that enable to perform separation of either instantaneous or real mixtures.Additionally, two post-processing techniques aimed at improving the isolation of the separated sources are proposed. The performance achieved by several SSS methods in the resynthesis of WFS sound scenes is afterwards evaluated by means of listening tests, paying special attention to the change observed in the perceived spatial attributes. Although the estimated sources are distorted versions of the original ones, the masking effects involved in their spatial remixing make artifacts less perceptible, which improves the overall assessed quality. Finally, some novel developments related to the application of time-frequency processing to source localization and enhanced sound reproduction are presented.Cobos Serrano, M. (2009). Application of sound source separation methods to advanced spatial audio systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8969Palanci

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

Speech enhancement algorithms for audiological applications

Texto en inglés y resumen en inglés y españolPremio Extraordinario de Doctorado de la UAH en el año académico 2013-2014La mejora de la calidad de la voz es un problema que, aunque ha sido abordado durante muchos años, aún sigue abierto. El creciente auge de aplicaciones tales como los sistemas manos libres o de reconocimiento de voz automático y las cada vez mayores exigencias de las personas con pérdidas auditivas han dado un impulso definitivo a este área de investigación. Esta tesis doctoral se centra en la mejora de la calidad de la voz en aplicaciones audiológicas. La mayoría del trabajo de investigación desarrollado en esta tesis está dirigido a la mejora de la inteligibilidad de la voz en audífonos digitales, teniendo en cuenta las limitaciones de este tipo de dispositivos. La combinación de técnicas de separación de fuentes y filtrado espacial con técnicas de aprendizaje automático y computación evolutiva ha originado novedosos e interesantes algoritmos que son incluidos en esta tesis. La tesis esta dividida en dos grandes bloques. El primer bloque contiene un estudio preliminar del problema y una exhaustiva revisión del estudio del arte sobre algoritmos de mejora de la calidad de la voz, que sirve para definir los objetivos de esta tesis. El segundo bloque contiene la descripción del trabajo de investigación realizado para cumplir los objetivos de la tesis, así como los experimentos y resultados obtenidos. En primer lugar, el problema de mejora de la calidad de la voz es descrito formalmente en el dominio tiempo-frecuencia. Los principales requerimientos y restricciones de los audífonos digitales son definidas. Tras describir el problema, una amplia revisión del estudio del arte ha sido elaborada. La revisión incluye algoritmos de mejora de la calidad de la voz mono-canal y multi-canal, considerando técnicas de reducción de ruido y técnicas de separación de fuentes. Además, la aplicación de estos algoritmos en audífonos digitales es evaluada. El primer problema abordado en la tesis es la separación de fuentes sonoras en mezclas infra-determinadas en el dominio tiempo-frecuencia, sin considerar ningún tipo de restricción computacional. El rendimiento del famoso algoritmo DUET, que consigue separar fuentes de voz con solo dos mezclas, ha sido evaluado en diversos escenarios, incluyendo mezclas lineales y binaurales no reverberantes, mezclas reverberantes, y mezclas de voz con otro tipo de fuentes tales como ruido y música. El estudio revela la falta de robustez del algoritmo DUET, cuyo rendimiento se ve seriamente disminuido en mezclas reverberantes, mezclas binaurales, y mezclas de voz con música y ruido. Con el objetivo de mejorar el rendimiento en estos casos, se presenta un novedoso algoritmo de separación de fuentes que combina la técnica de clustering mean shift con la base del algoritmo DUET. La etapa de clustering del algoritmo DUET, que esta basada en un histograma ponderado, es reemplazada por una modificación del algoritmo mean shift, introduciendo el uso de un kernel Gaussiano ponderado. El análisis de los resultados obtenidos muestran una clara mejora obtenida por el algoritmo propuesto en relación con el algoritmo DUET original y una modificación que usa k-means. Además, el algoritmo propuesto ha sido extendido para usar un array de micrófonos de cualquier tamaño y geometría. A continuación se ha abordado el problema de la enumeración de fuentes de voz, que esta relacionado con el problema de separación de fuentes. Se ha propuesto un novedoso algoritmo basado en un criterio de teoría de la información y en la estimación de los retardos relativos causados por las fuentes entre un par de micrófonos. El algoritmo ha obtenido excelente resultados y muestra robustez en la enumeración de mezclas no reverberantes de hasta 5 fuentes de voz. Además se demuestra la potencia del algoritmo para la enumeración de fuentes en mezclas reverberantes. El resto de la tesis esta centrada en audífonos digitales. El primer problema tratado es el de la mejora de la inteligibilidad de la voz en audífonos monoaurales. En primer lugar, se realiza un estudio de los recursos computacionales disponibles en audífonos digitales de ultima generación. Los resultados de este estudio se han utilizado para limitar el coste computacional de los algoritmos de mejora de la calidad de la voz para audífonos propuestos en esta tesis. Para resolver este primer problema se propone un algoritmo mono-canal de mejora de la calidad de la voz de bajo coste computacional. El objetivo es la estimación de una mascara tiempo-frecuencia continua para obtener el mayor parámetro PESQ de salida. El algoritmo combina una versión generalizada del estimador de mínimos cuadrados con un algoritmo de selección de características a medida, utilizando un novedoso conjunto de características. El algoritmo ha obtenido resultados excelentes incluso con baja relación señal a ruido. El siguiente problema abordado es el diseño de algoritmos de mejora de la calidad de la voz para audífonos binaurales comunicados de forma inalámbrica. Estos sistemas tienen un problema adicional, y es que la conexión inalámbrica aumenta el consumo de potencia. El objetivo en esta tesis es diseñar algoritmos de mejora de la calidad de la voz de bajo coste computacional que incrementen la eficiencia energética en audífonos binaurales comunicados de forma inalámbrica. Se han propuesto dos soluciones. La primera es un algoritmo de extremado bajo coste computacional que maximiza el parámetro WDO y esta basado en la estimación de una mascara binaria mediante un discriminante cuadrático que utiliza los valores ILD e ITD de cada punto tiempo-frecuencia para clasificarlo entre voz o ruido. El segundo algoritmo propuesto, también de bajo coste, utiliza además la información de puntos tiempo-frecuencia vecinos para estimar la IBM mediante una versión generalizada del LS-LDA. Además, se propone utilizar un MSE ponderado para estimar la IBM y maximizar el parámetro WDO al mismo tiempo. En ambos algoritmos se propone un esquema de transmisión eficiente energéticamente, que se basa en cuantificar los valores de amplitud y fase de cada banda de frecuencia con un numero distinto de bits. La distribución de bits entre frecuencias se optimiza mediante técnicas de computación evolutivas. El ultimo trabajo incluido en esta tesis trata del diseño de filtros espaciales para audífonos personalizados a una persona determinada. Los coeficientes del filtro pueden adaptarse a una persona siempre que se conozca su HRTF. Desafortunadamente, esta información no esta disponible cuando un paciente visita el audiólogo, lo que causa perdidas de ganancia y distorsiones. Con este problema en mente, se han propuesto tres métodos para diseñar filtros espaciales que maximicen la ganancia y minimicen las distorsiones medias para un conjunto de HRTFs de diseño