SPEECH ENHANCEMENT WITH SPARSE CODING IN LEARNED DICTIONARIES

The enhancement of speech degraded by non-stationary interferers is a highly relevant and difficult task of many signal processing applications. We present a monaural speech enhancement method based on sparse coding of noisy speech signals in a composite dictionary, consisting of the concatenation of a speech and interferer dictionary, both being possibly over-complete. The speech dictionary is learned off-line on a training corpus, while an environment specific interferer dictionary is learned on-line during speech pauses. Our approach optimizes the trade-off between source distortion and source confusion, and thus achieves significant improvements on objective quality measures like cepstral distance, in the speaker dependent and independent case, in several real-world environments and at low signal-to-noise ratios. Our enhancement method outperforms state-of-the-art methods like multi-band spectral subtraction and approaches based on vector quantization.