This page is the setup for subjective evaluation of our proposed blind source separation algorithm via model-based sparse recovery (BSS-MSR). The experiments are conducted on multi-channel original numbers corpus recorded at Idiap using an 8 channel circular microphone array in a moderately reverberant meeting room set-up. To compare with the alternative techniques for convolutive demixing, beamforming results has been also provided. The details of this study has been published in:
Multi-party Speech Recovery Exploiting Structured Sparsity Models, Afsaneh Asaei, Mohammad J. Taghizadeh, Hervé Bourlard and Volkan Cevher, International Speech Commuinication Association, INTERSPEECH 2011.
|Reference Distant Microphone|
|Reference Close Microphone|
|Reference Lapel Microphone|
|Near-field Superdirective Beamforming|
We further conducted speech recognition on MONC data base using Super-Directive (SD) beamformer, SD followed by McCowan post-filtering (SD-M), Speech dereverberation by Room Impulse Response (RIR) estimated and fitted Image Model in Least Squares sense (RIR-LS) as well as Room Acoustic Modeling via joint Sparse Recovery (RAM-SR). Please find the results as follow