A novel regularization framework for transient noise reduction

Abstract

In this paper, we propose a novel method for estimating clean speech from a single channel transient noise corrupted speech. In the proposed method we assume that speech spectrogram is both sparse and has temporal continuity property, and transient noise spectrogram is both sparse and has spectral continuity property. Based on these assumptions, we define a novel regularization model with sparsity and continuity imposing regularization terms for transient noise reduction. Then we solve the proposed model via alternating direction method of multipliers (ADMM) and derive an efficient iterative algorithm. Based on the assumption that transient noise spectrogram is low rank, we construct a binary mask that specifies locations of the transients and apply it in the proposed algorithm to achieve better separation results. Our method straightforwardly estimates speech and is free of noise power spectral density (PSD) estimation and does not need any pre-trained models of speech or noise. Experiments with various types of transient noises demonstrate effectiveness of the proposed method.