BLASTER: An Off-Grid Method for Blind and Regularized Acoustic Echoes Retrieval -- with supplementary material

Diego Di Carlo (1) , Clément Elvira (1) , Antoine Deleforge (2) , Nancy Bertin (1) , Rémi Gribonval (3)

1 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
3 DANTE - Dynamic Networks : Temporal and Structural Capture Approach

Acoustic echoes retrieval is a research topic that is gaining importance in many speech and audio signal processing applications such as speech enhancement, source separation, dereverberation and room geometry estimation. This work proposes a novel approach to blindly retrieve the off-grid timing of early acoustic echoes from a stereophonic recording of an unknown sound source such as speech. It builds on the recent framework of continuous dictionaries. In contrast with existing methods, the proposed approach does not rely on parameter tuning nor peak picking techniques by working directly in the parameter space of interest. The accuracy and ro-bustness of the method are assessed on challenging simulated setups with varying noise and reverberation levels and are compared to two state-of-the-art methods.

Mots clés: Acoustic Impulse Response; Sparsity; Super Resolution; Blind Channel Identification; Index Terms-Blind Channel Identification

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Diego Di Carlo, Clément Elvira, Antoine Deleforge, Nancy Bertin, Rémi Gribonval. BLASTER: An Off-Grid Method for Blind and Regularized Acoustic Echoes Retrieval -- with supplementary material. [Research Report] Inria. 2020. ⟨hal-02469901v2⟩