A perceptual subspace method for sinusoidal speech and audio modeling

The problem of modeling a signal segment as a sum of exponentially damped sinusoidal components is of interest in a wide range of fields, including speech and audio processing. Often, model parameters are estimated using subspace based techniques that exploit the so-called shift-invariance property. A drawback of these estimation techniques in relation to speech and audio processing is that the perceptual relevance of the model components is not taken into account. In this paper we show how to combine well-known subspace based estimation techniques with a recently developed perceptual distortion measure, to obtain an algorithm for extracting perceptually relevant model components. In analysis-synthesis experiments with wideband audio signals, objective and subjective evaluations show that the proposed algorithm improves perceived signal quality considerably over traditional subspace based analysis methods.