Multi-channel signal enhancement with speech and noise covariance estimates computed by a probabilistic localization model

• Published in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 156 - 160
• Main Authors: Anemuller, Jorn, Kayser, Hendrik
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-03-2017
• Subjects: Acoustic source localization; Acoustics; Covariance matrices; Estimation; Interference; machine learning for signal processing; multimicrophone signal enhancement; Signal to noise ratio; Speech; Speech enhancement
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7952137

Classic approaches to multi-channel signal enhancement rely on model assumptions regarding speech source relative transfer functions and noise covariance matrix, or on estimates thereof obtained in, e.g., speech pauses. To alleviate these constraints, we here investigate an approach to adaptive estimation of the speech (target) source and noise related acoustic parameters based on localized speech probability estimates. The latter are computed from a discriminatively trained speech localization algorithm previously proposed [1]. A-priori knowledge of temporal segments that contain noise only, thus, is not required. A standard MVDR system is employed for subsequent signal enhancement. Evaluation is carried out for anechoic and reverberant conditions using 6-channel input signals recorded with a bilateral hearing-aid geometry. Results indicate that the proposed method outperforms an anechoic, isotropic-noise model when a-priori information is unavailable: I.e., in (a) anechoic conditions with localized interferer in addition to isotropic noise, and (b) reverberant conditions. In these conditions, the proposed method and constrained versions thereof improve upon the free-field isotropic noise model by up to 16.1 dB and 7.7 dB SINR, respectively.