Blind audio source separation (BASS) arises in a
number of applications in speech and music processing such
as speech enhancement, speaker diarization, automated music
transcription etc. Generally, BASS methods consider multichannel
signal capture. The single microphone case is the most
difficult underdetermined case, but it often arises in practice.
In the approach considered here, the main source identifiability
comes from exploiting the presumed quasi-periodic nature of
sources via long-term autoregressive (AR) modeling. Indeed,
musical note signals are quasi-periodic and so is voiced speech,
which constitutes the most energetic part of speech signals. We
furthermore exploit (e.g. speaker or instrument related) prior
information in the spectral envelope of the source signals via
short-term AR modeling. We present an iterative method based
on the minimization of the Itakura-Saito distance for estimating
the sources parameters directly from the mixture using a frame
based analysis.
