Computational methods for underdetermined convolutive speech localization and separation via model-based sparse component analys

In this paper, the problem of speech source localization and separation from recordings of convolutive underdetermined mixtures is studied. The problem is cast as recovering the spatio-spectral speech information embedded in a microphone array compressed measurements of the acoustic field. A model-based sparse component analysis framework is formulated for sparse reconstruction of the speech spectra in a reverberant acoustic resulting in joint localization and separation of the individual sources. We compare and contrast the computational approaches to model-based sparse recovery exploiting spatial sparsity as well as spectral structures underlying spectrographic representation of speech signals. In this context, we explore identification of the sparsity structures at the auditory and acoustic representation spaces. The auditory structures are formulated upon the principles of structural grouping based on proximity, autoregressive correlation and harmonicity of the spectral coeffici...

• Type: Journal
• Year: 2016
• Where: SPEECH
• Authors: Afsaneh Asaei, Hervé Bourlard, Mohammad Javad Taghizadeh, Volkan Cevher
• Keywords: Security Privacy, SPEECH 2016