Multi-Sensor Spectro-Temporal Comb Filtering for Speech Enhancement

Abstract

Comb filters are popular in the speech enhancement field because of their ability to suppress noise in the voiced speech spectrum without degrading the speech quality. Adaptive comb filters (ACF) can suppress significant amounts of noise in voiced speech using the quasiperiodic properties of the voiced speech sounds. However, they rely on accurate voicing and pitch epoch detection, which is challenging at low SNRs and in nonstationary noise conditions. Here, we propose using a non-acoustic auxiliary sensor for detecting the pitch epochs and voicing. Experiment results with the ACF filter showed practically no noise reduction when noisy speech is used for pitch epoch and voicing detection. However, when the auxiliary sensor signal is used, significant improvements are obtained. Therefore, it is shown that the ACF system becomes useful, at least under the considered noise conditions, only if the auxiliary data is available. In addition to the dramatic gain with the auxiliary sensors, performance of the ACF system is further boosted by using it in tandem with a frequency-domain ACF system proposed here. Objective measures, spectrogram analysis, and subjective listening test results clearly show substantial improvement with the tandem system compared to the time-domain ACF system.