Abstract: A method for reverberation suppression may involve receiving an input audio signal. The method may involve classifying a media type of the input audio signal as one of a group comprising at least: 1) speech; 2) music; or 3) speech over music. The method may involve determining whether to perform dereverberation on the input audio signal based at least on a determination that the media type of the input audio signal has been classified as speech. The method may involve generating an output audio signal by performing dereverberation on the input audio signal in response to determining that dereverberation is to be performed on the input audio signal.

Abstract: Disclosed is a method for determining one or more dialog quality metrics of a mixed audio signal comprising a dialog component and a noise component, the method comprising separating an estimated dialog component from the mixed audio signal by means of a dialog separator using a dialog separating model determined by training the dialog separator based on the one or more quality metrics; providing the estimated dialog component from the dialog separator to a quality metrics estimator; and determining the one or more quality metrics by means of the quality metrics estimator based on the mixed signal and the estimated dialog component. Further disclosed is a method for training a dialog separator, a system comprising circuitry configured to perform the method, and a non-transitory computer-readable storage medium.

Abstract: Described herein is a method for Convolutional Neural Network (CNN) based speech source separation, wherein the method includes the steps of: (a) providing multiple frames of a time-frequency transform of an original noisy speech signal; (b) inputting the time-frequency transform of said multiple frames into an aggregated multi-scale CNN having a plurality of parallel convolution paths; (c) extracting and outputting, by each parallel convolution path, features from the input time-frequency transform of said multiple frames; (d) obtaining an aggregated output of the outputs of the parallel convolution paths; and (e) generating an output mask for extracting speech from the original noisy speech signal based on the aggregated output. Described herein are further an apparatus for CNN based speech source separation as well as a respective computer program product comprising a computer-readable storage medium with instructions adapted to carry out said method when executed by a device having processing capability.