“Discriminative feature domains for reverberant acoustic environments”

By |2018-01-16T22:09:58+00:00June 19th, 2017|

IEEE Xplore Access:

in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)

Authors: 

C. Papayiannis, C. Evers, and P. A. Naylor

Abstract:

Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment identification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Machine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Error Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Experimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications.

“Source tracking using moving microphone arrays for robot audition”

By |2018-01-16T22:10:07+00:00June 19th, 2017|

IEEE Xplore Access:

Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)

Authors:

C. Evers, Y. Dorfan, S. Gannot, and P. A. Naylor

Abstract:

Intuitive spoken dialogues are a prerequisite for human-robot interaction. In many practical situations, robots must be able to identify and focus on sources of interest in the presence of interfering speakers. Techniques such as spatial filtering and blind source separation are therefore often used, but rely on accurate knowledge of the source location. In practice, sound emitted in enclosed environments is subject to reverberation and noise. Hence, sound source localization must be robust to both diffuse noise due to late reverberation, as well as spurious detections due to early reflections. For improved robustness against reverberation, this paper proposes a novel approach for sound source tracking that constructively exploits the spatial diversity of a microphone array installed in a moving robot. In previous work, we developed speaker localization approaches using expectation-maximization (EM) approaches and using Bayesian approaches. In this paper we propose to combine the EM and Bayesian approach in one framework for improved robustness against reverberation and noise.
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