Parallel Dictionary Learning for Multimodal Voice Conversion Using Matrix Factorization

2016 ◽  
pp. 27-40 ◽  
Author(s):  
Ryo Aihara ◽  
Kenta Masaka ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Dictionary Learning ◽  
Matrix Factorization ◽  
Voice Conversion

scholarly journals Non-parallel dictionary learning for voice conversion using non-negative Tucker decomposition

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yuki Takashima ◽  
Toru Nakashika ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Dictionary Learning ◽  
Computational Cost ◽  
Tensor Decomposition ◽  
Gaussian Mixture ◽  
Voice Conversion ◽  
Specific Information ◽  
Learning Method ◽  
Tucker Decomposition ◽  
Parallel Data ◽  
High Computational Cost

Abstract Voice conversion (VC) is a technique of exclusively converting speaker-specific information in the source speech while preserving the associated phonemic information. Non-negative matrix factorization (NMF)-based VC has been widely researched because of the natural-sounding voice it achieves when compared with conventional Gaussian mixture model-based VC. In conventional NMF-VC, models are trained using parallel data which results in the speech data requiring elaborate pre-processing to generate parallel data. NMF-VC also tends to be an extensive model as this method has several parallel exemplars for the dictionary matrix, leading to a high computational cost. In this study, an innovative parallel dictionary-learning method using non-negative Tucker decomposition (NTD) is proposed. The proposed method uses tensor decomposition and decomposes an input observation into a set of mode matrices and one core tensor. The proposed NTD-based dictionary-learning method estimates the dictionary matrix for NMF-VC without using parallel data. The experimental results show that the proposed method outperforms other methods in both parallel and non-parallel settings.

scholarly journals Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zunyi Tang ◽  
Shuxue Ding ◽  
Zhenni Li ◽  
Linlin Jiang
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Coordinate Descent ◽  
Multispectral Data ◽  
Overcomplete Dictionary ◽  
Novel Method ◽  
Better Than

Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF) with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL) algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD) algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.

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