Audio source separation based on flexible pre-trained probabilistic source models

Abstract

Improved audio source separation is provided by providing an audio dictionary for each source to be separated. Thus the invention can be regarded as providing 'partially blind' source separation as opposed to the more commonly considered 'blind' source separation problem, where no prior information about the sources is given. The audio dictionaries are probabilistic source models, and can be derived from training data from the sources to be separated, or from similar sources. Thus a library of audio dictionaries can be developed to aid in source separation. An unmixing and deconvolutive transformation can be inferred by maximum likelihood (ML) given the received signals and the selected audio dictionaries as input to the ML calculation. Optionally, frequency-domain filtering of the separated signal estimates can be performed prior to reconstructing the time-domain separated signal estimates. Such filtering can be regarded as providing an “audio skin” for a recovered signal.