CONF Asaei_ICASSP_2014/IDIAP Model-based Sparse Component Analysis for Reverberant Speech Localization Asaei, Afsaneh Bourlard, Hervé Taghizadeh, Mohammad J. Cevher, Volkan Ad-hoc microphone array Autoregressive modeling Model-based sparse recovery Reverberation Room acoustic characterization Speech source localization Structured sparsity EXTERNAL http://publications.idiap.ch/attachments/papers/2014/Asaei_ICASSP_2014.pdf PUBLIC 2014 IEEE International Conference on Acoustics, Speech and Signal Processing 2014 IEEE 1439 - 1443 1520-6149 10.1109/ICASSP.2014.6853835 doi 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.