We study the sparsity of spectro-temporal representation of speech in reverberant acoustic conditions. This study motivates the use of structured sparsity models for efficient speech recovery. We formulate the underdetermined convolutive speech separation in spectro-temporal domain as the sparse signal recovery where we leverage model-based recovery algorithms. To tackle the ambiguity of the real acoustics, we exploit the Image Model of the enclosures to estimate the room impulse response function through a structured sparsity constraint optimization. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech applications.
Bibliographic reference. Asaei, Afsaneh / Taghizadeh, Mohammad J. / Bourlard, Hervé / Cevher, Volkan (2011): "Multi-party speech recovery exploiting structured sparsity models", In INTERSPEECH-2011, 185-188.