Blind signal processing methods for microphone leakage suppression in multichannel audio applications

This thesis examines the problem of microphone leakage, that is the interference between simultaneously active sound sources in multichannel audio applications. Despite being a common problem with which sound engineers are confronted every day, almost no signal processing methods have been proposed to address this issue. In this work, the problem is formulated for the first time in a signal processing framework. First, it formulated inside the blind source separation (BSS) context and the limitations of related methods are analysed and reported. Since, BSS methods seem to be inappropriate for this specific problem, it is reformulated as a signal in noise problem inside the well-known noise suppression framework. Based on the widely adopted close-microphone technique a novel, generalized framework for leakage suppression is derived based on a multichannel Wiener filter. The acoustic system that models the mixing process is analysed and the related room impulse responses are discerned in direct and leakage acoustic paths. The properties of the direct acoustic path, that is the close-microphone response are investigated for the first time, from a signal processing point of view as well as a room acoustics perspective. The properties of the leakage acoustic path are also analysed for the first time using room acoustic parameters. After key properties of the acoustic paths have been identified, a method for the suppression of microphone leakage in a two channel audio setup is developed based on a Wiener filter and a crude approximation of the related power spectral densities (PSDs). The performance of this method for actual recordings in real reverberant environments is more than adequate and based on these results, the method is extended for more than two sources and microphones in arbitrary arrangements. The complete method is blind and automatic, since it does not require any user input. It does not assume any prior knowledge or require training and is computationally efficient. A novel solo detection method has been developed that allows the estimation of weighting coefficients that correspond to the relative attenuation experienced by sound sources as they travel to each microphone. Combined with a new and advanced PSD estimation method based on the identification of dominant frequency bins, the related PSDs in a multichannel audio application can be identified. From these an appropriate multichannel Wiener filter for each microphone signal can be calculated, which will provide the estimated source signal at its output.