A multimodal approach to blind source separation of moving sources

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying; thus, the unmixing filters should also be time varying, which are difficult to calculate in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on video cameras. Positions and velocities of the sources are obtained from the 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary for a certain minimum period, a frequency domain BSS algorithm is implemented with an initialization derived from the positions of the source signals. Once the sources are moving, a beamforming algorithm which requires no prior statistical knowledge is used to perform real time speech enhancement and provide separation of the sources. Experimental results confirm that by utilizing the visual modality, the proposed algorithm not only improves the performance of the BSS algorithm and mitigates the permutation problem for stationary sources, but also provides a good BSS performance for moving sources in a low reverberant environment

Review of Research on Speech Technology: Main Contributions From Spanish Research Groups

In the last two decades, there has been an important increase in research on speech technology in Spain, mainly due to a higher level of funding from European, Spanish and local institutions and also due to a growing interest in these technologies for developing new services and applications. This paper provides a review of the main areas of speech technology addressed by research groups in Spain, their main contributions in the recent years and the main focus of interest these days. This description is classified in five main areas: audio processing including speech, speaker characterization, speech and language processing, text to speech conversion and spoken language applications. This paper also introduces the Spanish Network of Speech Technologies (RTTH. Red Temática en Tecnologías del Habla) as the research network that includes almost all the researchers working in this area, presenting some figures, its objectives and its main activities developed in the last years

Towards Computer Understanding of Human Interactions

People meet in order to interact - disseminating information, making decisions, and creating new ideas. Automatic analysis of meetings is therefore important from two points of view: extracting the information they contain, and understanding human interaction processes. Based on this view, this article presents an approach in which relevant information content of a meeting is identified from a variety of audio and visual sensor inputs and statistical models of interacting people. We present a framework for computer observation and understanding of interacting people, and discuss particular tasks within this framework, issues in the meeting context, and particular algorithms that we have adopted. We also comment on current developments and the future challenges in automatic meeting analysis

Video-aided model-based source separation in real reverberant rooms

Source separation algorithms that utilize only audio data can perform poorly if multiple sources or reverberation are present. In this paper we therefore propose a video-aided model-based source separation algorithm for a two-channel reverberant recording in which the sources are assumed static. By exploiting cues from video, we first localize individual speech sources in the enclosure and then estimate their directions. The interaural spatial cues, the interaural phase difference and the interaural level difference, as well as the mixing vectors are probabilistically modeled. The models make use of the source direction information and are evaluated at discrete timefrequency points. The model parameters are refined with the wellknown expectation-maximization (EM) algorithm. The algorithm outputs time-frequency masks that are used to reconstruct the individual sources. Simulation results show that by utilizing the visual modality the proposed algorithm can produce better timefrequency masks thereby giving improved source estimates. We provide experimental results to test the proposed algorithm in different scenarios and provide comparisons with both other audio-only and audio-visual algorithms and achieve improved performance both on synthetic and real data. We also include dereverberation based pre-processing in our algorithm in order to suppress the late reverberant components from the observed stereo mixture and further enhance the overall output of the algorithm. This advantage makes our algorithm a suitable candidate for use in under-determined highly reverberant settings where the performance of other audio-only and audio-visual methods is limited

Multimodal methods for blind source separation of audio sources

The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance. [Continues.

Continuous Microphone Array Speech Recognition on Wall Street Journal Corpus

In this paper, we present a robust speech acquisition system to acquire continuous speech using a microphone array. A microphone array based speech recognition system is also presented to study the environmental interference due to reverberation, background noises and mismatch between the training and testing conditions. This is important in the context of smart meeting rooms of Augmented MultiParty Interaction (AMI) project which aims at significant development of conversational speech recognition. In this regard, an audio-visual database containing the Wall Street journal phrases was recorded in a real meeting room for the stationary speaker, moving speaker and overlapping speech scenarios. We carried out speech enhancement and continuous speech recognition experiments on stationary speaker data. Using a microphone array with beamformer followed by a postfilter enhances speech quality slightly inferior to that of close-talk headset,and better than lapel. We achieved a significant reduction in word error rates using models adapted based on maximum linear likelihood regression (MLLR) and maximum-a-posteriori (MAP) approaches. Though the error rates of the microphone array data are larger than those of headset data, they are significantly smaller compared to the error rates of lapel data