Spectral Enhancement Methods

In this chapter, we focus on the statistical methods that constitute a speech spectral enhancement system and describe some of their fundamental components. We begin in Sect. 44.2 by formulating the problem of spectral enhancement. In Sect. 44.3, we address the time-frequency correlation of spectral coefficients for speech and noise signals, and present statistical models that conform with these characteristics. In Sect. 44.4, we present estimators for speech spectral coefficients under speech presence uncertainty based on various fidelity criteria. In Sect. 44.5, we address the problem of speech presence probability estimation. In Sect. 44.6, we present useful estimators for the a priori signal-to-noise ratio (SNR) under speech presence uncertainty. We present the decision-directed approach, which is heuristically motivated, and the recursive estimation approach, which is based on statistical models and follows the rationale of Kalman filtering. improved minima-controlled recursive averaging (IMCRA) In Sect. 44.7, we describe the improved minima-controlled recursive averaging (IMCRA) approach for noise power spectrum estimation. In Sect. 44.8, we provide a detailed example of a speech enhancement algorithm, and demonstrate its performance in environments with various noise types. In Sect. 44.9, we survey the main types of spectral enhancement components, and discuss the significance of the choice of statistical model, fidelity criterion, a priori SNR estimator, and noise spectrum estimator. Some concluding comments are made in Sect. 44.10.