UNSUPERVISED MONAURAL SPEECH ENHANCEMENT USING ROBUST NMF WITH LOW-RANK AND SPARSE CONSTRAINTS

Non-negative spectrogram decomposition and its variants have been extensively investigated for speech enhancement due to their efficiency in extracting perceptually meaningful components from mixtures. Usually, these approaches are implemented on the condition that training samples for one or more sources are available beforehand. However, in many real-world scenarios, it is always impossible for conducting any prior training. To solve this problem, we proposed an approach which directly extracts the representations of background noises from the noisy speech via imposing non-negative constraints on the low-rank and sparse decomposition of the noisy spectrogram. The noise representations are subsequently utilized when estimating the clean speech. In this technique, potential spectral structural regularity could be discovered for better reconstruction of clean speech. Evaluations on the Noisex-92 and TIMIT database showed that the proposed method achieves significant improvements over the state-of-the-art methods in unsupervised speech enhancement.