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 the 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 (weighted) Itakura-Saito distance
for estimating the source parameters directly from the mixture using
frame based processing.