Blind audio source separation using Short+Long Term AR source models and spectrum matching

Schutz, Antony; Slock, Dirk T M
DSP/SPE 2011, 14th IEEE Digital Signal Processing & 6th Signal Processing Education Workshop, January 4-7, 2011, Sedona, Arizona, USA

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.

Communication systems
Eurecom Ref:
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