Elias Kokkinis

A Wiener Filter Approach to Microphone Leakage Reduction in Close-Microphone Applications

Microphone leakage is one of the most prevalent problems in audio applications involving multiple instruments and multiple microphones. Currently, sound engineers have limited solutions available to them. In this paper, the applicability of two widely used signal enhancement methods to this problem is discussed, namely blind source separation and noise suppression. By extending previous work, it is shown that the noise suppression framework is a valid choice and can effectively address the problem of microphone leakage. Here, an extended form of the single channel Wiener filter is used which takes into account the individual audio sources to derive a multichannel noise term. A novel power spectral density (PSD) estimation method is also proposed based on the identification of dominant frequency bins by examining the microphone and output signal PSDs. The performance of the method is examined for simulated environments with various source-microphone setups and it is shown that the proposed approach efficiently suppresses leakage.

Speech dereverberation based on a recorded handclap

A semi-blind framework for the suppression of late speech reverberation is presented. The method is based on spectral subtraction and utilizes a simple recorded handclap to estimate the Power Spectral Density (PSD) of late reverberation. A statistical analysis of measured Room Impulse Responses (RIRs) and recorded handclaps demonstrates the sufficiency of the above estimation. Dereverberation results show that the proposed technique achieves significant reverberation suppression at multiple speaker positions in each room without compromising the quality of the estimated signals.