Localization of Directional Sound Sources Supported by a priori Information of the Acoustic Environment

Speaker localization with microphone arrays has received significant attention in the past decade as a means for automated speaker tracking of individuals in a closed space for videoconferencing systems, directed speech capture systems, and surveillance systems. Traditional techniques are based on estimating the relative time difference of arrivals (TDOA) between different channels, by utilizing crosscorrelation function. As we show in the context of speaker localization, these estimates yield poor results, due to the joint effect of reverberation and the directivity of sound sources. In this paper, we present a novel method that utilizes a priori acoustic information of the monitored region, which makes it possible to localize directional sound sources by taking the effect of reverberation into account. The proposed method shows significant improvement of performance compared with traditional methods in “noise-free” condition. Further work is required to extend its capabilities to noisy environments

Mathematical modelling ano optimization strategies for acoustic source localization in reverberant environments

La presente Tesis se centra en el uso de técnicas modernas de optimización y de procesamiento de audio para la localización precisa y robusta de personas dentro de un entorno reverberante dotado con agrupaciones (arrays) de micrófonos. En esta tesis se han estudiado diversos aspectos de la localización sonora, incluyendo el modelado, la algoritmia, así como el calibrado previo que permite usar los algoritmos de localización incluso cuando la geometría de los sensores (micrófonos) es desconocida a priori. Las técnicas existentes hasta ahora requerían de un número elevado de micrófonos para obtener una alta precisión en la localización. Sin embargo, durante esta tesis se ha desarrollado un nuevo método que permite una mejora de más del 30\% en la precisión de la localización con un número reducido de micrófonos. La reducción en el número de micrófonos es importante ya que se traduce directamente en una disminución drástica del coste y en un aumento de la versatilidad del sistema final. Adicionalmente, se ha realizado un estudio exhaustivo de los fenómenos que afectan al sistema de adquisición y procesado de la señal, con el objetivo de mejorar el modelo propuesto anteriormente. Dicho estudio profundiza en el conocimiento y modelado del filtrado PHAT (ampliamente utilizado en localización acústica) y de los aspectos que lo hacen especialmente adecuado para localización. Fruto del anterior estudio, y en colaboración con investigadores del instituto IDIAP (Suiza), se ha desarrollado un sistema de auto-calibración de las posiciones de los micrófonos a partir del ruido difuso presente en una sala en silencio. Esta aportación relacionada con los métodos previos basados en la coherencia. Sin embargo es capaz de reducir el ruido atendiendo a parámetros físicos previamente conocidos (distancia máxima entre los micrófonos). Gracias a ello se consigue una mejor precisión utilizando un menor tiempo de cómputo. El conocimiento de los efectos del filtro PHAT ha permitido crear un nuevo modelo que permite la representación 'sparse' del típico escenario de localización. Este tipo de representación se ha demostrado ser muy conveniente para localización, permitiendo un enfoque sencillo del caso en el que existen múltiples fuentes simultáneas. La última aportación de esta tesis, es el de la caracterización de las Matrices TDOA (Time difference of arrival -Diferencia de tiempos de llegada, en castellano-). Este tipo de matrices son especialmente útiles en audio pero no están limitadas a él. Además, este estudio transciende a la localización con sonido ya que propone métodos de reducción de ruido de las medias TDOA basados en una representación matricial 'low-rank', siendo útil, además de en localización, en técnicas tales como el beamforming o el autocalibrado

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Multiple source localization using spherical microphone arrays

Direction-of-Arrival (DOA) estimation is a fundamental task in acoustic signal processing and is used in source separation, localization, tracking, environment mapping, speech enhancement and dereverberation. In applications such as hearing aids, robot audition, teleconferencing and meeting diarization, the presence of multiple simultaneously active sources often occurs. Therefore DOA estimation which is robust to Multi-Source (MS) scenarios is of particular importance. In the past decade, interest in Spherical Microphone Arrays (SMAs) has been rapidly grown due to its ability to analyse the sound field with equal resolution in all directions. Such symmetry makes SMAs suitable for applications in robot audition where potential variety of heights and positions of the talkers are expected. Acoustic signal processing for SMAs is often formulated in the Spherical Harmonic Domain (SHD) which describes the sound field in a form that is independent of the geometry of the SMA. DOA estimation methods for the real-world scenarios address one or more performance degrading factors such as noise, reverberation, multi-source activity or tackled problems such as source counting or reducing computational complexity. This thesis addresses various problems in MS DOA estimation for speech sources each of which focuses on one or more performance degrading factor(s). Firstly a narrowband DOA estimator is proposed utilizing high order spatial information in two computationally efficient ways. Secondly, an autonomous source counting technique is proposed which uses density-based clustering in an evolutionary framework. Thirdly, a confidence metric for validity of Single Source (SS) assumption in a Time-Frequency (TF) bin is proposed. It is based on MS assumption in a short time interval where the number and the TF bin of active sources are adaptively estimated. Finally two analytical narrowband MS DOA estimators are proposed based on MS assumption in a TF bin. The proposed methods are evaluated using simulations and real recordings. Each proposed technique outperforms comparative baseline methods and performs at least as accurately as the state-of-the-art.Open Acces