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 [BibTeX] [Marc21]
Model-based Sparse Component Analysis for Reverberant Speech Localization
Type of publication: Conference paper
Citation: Asaei_ICASSP_2014
Publication status: Published
Booktitle: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2014
Month: May
Pages: 1439 - 1443
Publisher: IEEE
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2014.6853835
Abstract: This paper proposes a speech localization framework based on model-based sparse recovery. We compare and contrast the computational sparse optimization methods incorporating harmonicity and block structures as well as autoregressive dependencies underlying spectrographic representation of speech signals. Extensive evaluations are conducted to quantify the performance bound for localization of multiple sources from underdetermined mixtures in a reverberant environment. The results demonstrate the effectiveness of sparse Bayesian learning framework for speech source localization. Furthermore, the importance of construction layout of microphone array is investigated. The outcome of this study encourages the use of ad-hoc microphones for the data acquisition set-up.
Keywords: Ad-hoc microphone array, Autoregressive modeling, Model-based sparse recovery, Reverberation, Room acoustic characterization, Speech source localization, Structured sparsity
Projects Idiap
Authors Asaei, Afsaneh
Bourlard, Hervé
Taghizadeh, Mohammad J.
Cevher, Volkan
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  • Asaei_ICASSP_2014.pdf