Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Patrick J. Wolfe, Simon J. Godsill
The Bayesian paradigm provides a natural and effective means of exploit- ing prior knowledge concerning the time-frequency structure of sound signals such as speech and music—something which has often been over- looked in traditional audio signal processing approaches. Here, after con- structing a Bayesian model and prior distributions capable of taking into account the time-frequency characteristics of typical audio waveforms, we apply Markov chain Monte Carlo methods in order to sample from the resultant posterior distribution of interest. We present speech enhance- ment results which compare favourably in objective terms with standard time-varying filtering techniques (and in several cases yield superior per- formance, both objectively and subjectively); moreover, in contrast to such methods, our results are obtained without an assumption of prior knowledge of the noise power.