Microphone array post-filtering using supervised machine learning for speech enhancement

High level of noise reduces the perceptual quality and intelligibility of speech. Therefore, enhancing the captured speech signal is important in everyday applications such as telephony and teleconferencing. Microphone arrays are typically placed at a distance from a speaker and require processing to enhance the captured signal. Beamforming provides directional gain towards the source of interest and attenuation of interference. It is often followed by a single channel post-filter to further enhance the signal. Non-linear spatial post-filters are capable of providing high noise suppression but can produce unwanted musical noise that lowers the perceptual quality of the output. This work proposes an artificial neural network (ANN) to learn the structure of naturally occurring post-filters to enhance speech from interfering noise. The ANN uses phase-based features obtained from a multichannel array as an input. Simulations are used to train the ANN in a supervised manner. The performance is measured with objective scores from speech recorded in an office environment. The post-filters predicted by the ANN are found to improve the perceptual quality over delay-and-sum beamforming while maintaining high suppression of noise characteristic to spatial post-filters.