Modified coherence‐based dictionary learning method for speech enhancement

Abstract

This paper presents a new method for speech enhancement based on a dictionary learning method. The proposed approach is based on using coherence measure in dictionary learning. Data required for better fitting to atoms in sparse representation of noise is provided by a noise estimation algorithm that causes noise dictionary to be trained with the same data size as speech signal. To decrease coherence between dictionaries after the training step, a new method is applied to yield incoherent dictionaries. In sparse representation of speech data, the highest energy atoms of noise dictionary are replaced with the lowest energy atoms, under certain conditions. A similar replacement can happen in sparse representation of noise data. Furthermore, in this paper, only one noise dictionary, chosen by a classification method, is used in speech enhancement step, resulting in a faster algorithm. Objective and subjective measures are used for evaluating the simulation results. According to experimental results, the proposed algorithm has been found superior in performance and computation overhead in comparison with the earlier methods in this context. Moreover, this method achieves significantly better results compared with baseline methods such as multiband and geometric spectral subtraction.

Optimal Bayesian Speech Enhancement by Parametric Joint Detection and Estimation

“…In this case, we want to minimize the estimation cost. According to [31,Theorem 2], there exists a solution (ψ * 1 , ψ * 0 , δ * ) to (5). To present this solution, we need to introduce the conditional risks…”

“…Despite good results obtained by machine learning approaches (see [1]- [3] for deep neural network or [4], [5] for dictionary-based methods), there is still room for unsupervised techniques, especially in applications where large enough databases are hardly available for all the types of noise and speech signals that can actually be encountered [6], [7]. This is the case in assisted listening for The associate editor coordinating the review of this manuscript and approving it for publication was Md.…”

Dictionary Learning-Based Speech Enhancement

“…Nonnegative matrix factorization (NMF) can be viewed as an approach for dictionary learning. NMF, first introduced by Paatero and Tapper [36] and later popularized by Lee and Seung [23,[27][28][29][30][31][32][33][34][35][36][37], has been known as a part-based representation model. Different to other matrix factorization approaches, NMF takes into account the fact that most types of real-world data, particularly sound and videos, are nonnegative and maintain such nonnegativity constraints in factorization.…”

“…where c [i] is the ith row of C. The residual norm is minimized by seeking for a rank-one approximation [35]. The approximation is based on computing the singular value decomposition (SVD) [23].…”

Speech Enhancement Based on Sparse Representation Using Joint Dictionary

“…The residual norm is minimized by seeking for a rank-one approximation [12]. The approximation is based on computing the singular value decomposition (SVD) [13].…”