Exponentiated Geometric-Mean-Balanced Mask for Underdetermined Source Separation
2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), Oct 12, 2021
In deep-neural-network-based mask estimation for estimating the probability of speech presence fo... more In deep-neural-network-based mask estimation for estimating the probability of speech presence for each time-frequency bin, mask estimation is biased because of the sparsity of the speech components in the bins. For unbiased estimation, we propose a method for simultaneously estimating a speech mask and its inverse (noise) mask and obtaining enhancement-mask from the speech and inverse masks’ geometric means after exponentiation to enable easier mask estimation. Experiments on underdetermined source separation confirmed the effectiveness of our mask-estimation method.
Multi-channel non-negative matrix factorization (MNMF) is one of the most effective blind source ... more Multi-channel non-negative matrix factorization (MNMF) is one of the most effective blind source separation techniques. This paper proposes a stable initialization method of MNMF by accurately estimating a full-rank spatial correlation matrix. Alternative initialization can be a rank-1 spatial correlation matrix to be obtained as an outer product of a steering vector, which is an eigenvector that corresponds to the maximum eigenvalue of a spatial correlation matrix. This paper compares full-rank and rank-1 types of initialization. On the other hand, independent low-rank matrix analysis (ILRMA) accelerates the matrix factorization by using a rank-1 demixing matrix instead of a spatial correlation matrix. The above-mentioned initialization method can be applied to ILRMA. The drawback of ILRMA is an overdetermined situation where the number of observations is greater than that of sources. In such cases, special treatments are necessary for ILRMA to match the number of observations to the number of sources, whereas MNMF can deal with such cases naturally. Experiments on a noisy speech recognition task showed the effectiveness of the proposed initialization method both for MNMF and ILRMA. For determined cases, ILRMA was faster and better than MNMF, but for overdetermined cases, even with special treatments, ILRMA was inferior to MNMF.
This paper proposes an effective sequential initialization for multichannel nonnegative matrix fa... more This paper proposes an effective sequential initialization for multichannel nonnegative matrix factorization to address the difficulty of initial value dependency of the conventional method. The proposed method sets initial values of parameters from those obtained for the smaller number of channels. The experimental results of music separation show that the proposed method outperforms the conventional method.
Multi-channel non-negative matrix factorization with binary mask initialization for automatic speech recognition
Journal of the Acoustical Society of America, Oct 1, 2016
Non-negative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative ma... more Non-negative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative matrices. In the field of acoustics, multichannel expansion has been proposed to consider spatial information for sound source separation. Conventional multi-channel NMF has a difficulty in an initial-value dependency of the separation performance due to local minima. This paper proposes initial value settings by using binary masking based sound source separation whose masks on the time frequency domain are calculated from the time difference of arrival of each source. The proposed method calculates initial spatial correlation matrices using separated sources by binary masking. The music separation experiments confirmed that the separation performance of the proposed method was better than that of the conventional method. In addition, we evaluated initial value settings by using binary masking for automatic speech recognition (ASR) tasks in noisy environments. The ASR experiments confirmed that appropriate initial value settings were effective because initializations starting from previously well-estimated spatial correlation matrices achieved better ASR performances than random initializations.
Privacy preservation satisfying utility requirements based on multi-objective optimization
2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), Nov 29, 2022
We developed a dialogue system for Dialogue Robot Competition 2022. Our system is composed of thr... more We developed a dialogue system for Dialogue Robot Competition 2022. Our system is composed of three parts. First part investigates participants' demographic information by rule-based interview. Second part recommends a point of interest (POI) based on the collected demographic information. Third part answers participants' question based on the combination of rule-based answering and deep-learningbased answering with nearby POI search.
Exponentiated Geometric-Mean-Balanced Mask for Underdetermined Source Separation
2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), Oct 12, 2021
In deep-neural-network-based mask estimation for estimating the probability of speech presence fo... more In deep-neural-network-based mask estimation for estimating the probability of speech presence for each time-frequency bin, mask estimation is biased because of the sparsity of the speech components in the bins. For unbiased estimation, we propose a method for simultaneously estimating a speech mask and its inverse (noise) mask and obtaining enhancement-mask from the speech and inverse masks’ geometric means after exponentiation to enable easier mask estimation. Experiments on underdetermined source separation confirmed the effectiveness of our mask-estimation method.
Multi-channel non-negative matrix factorization (MNMF) is one of the most effective blind source ... more Multi-channel non-negative matrix factorization (MNMF) is one of the most effective blind source separation techniques. This paper proposes a stable initialization method of MNMF by accurately estimating a full-rank spatial correlation matrix. Alternative initialization can be a rank-1 spatial correlation matrix to be obtained as an outer product of a steering vector, which is an eigenvector that corresponds to the maximum eigenvalue of a spatial correlation matrix. This paper compares full-rank and rank-1 types of initialization. On the other hand, independent low-rank matrix analysis (ILRMA) accelerates the matrix factorization by using a rank-1 demixing matrix instead of a spatial correlation matrix. The above-mentioned initialization method can be applied to ILRMA. The drawback of ILRMA is an overdetermined situation where the number of observations is greater than that of sources. In such cases, special treatments are necessary for ILRMA to match the number of observations to the number of sources, whereas MNMF can deal with such cases naturally. Experiments on a noisy speech recognition task showed the effectiveness of the proposed initialization method both for MNMF and ILRMA. For determined cases, ILRMA was faster and better than MNMF, but for overdetermined cases, even with special treatments, ILRMA was inferior to MNMF.
This paper proposes an effective sequential initialization for multichannel nonnegative matrix fa... more This paper proposes an effective sequential initialization for multichannel nonnegative matrix factorization to address the difficulty of initial value dependency of the conventional method. The proposed method sets initial values of parameters from those obtained for the smaller number of channels. The experimental results of music separation show that the proposed method outperforms the conventional method.
Multi-channel non-negative matrix factorization with binary mask initialization for automatic speech recognition
Journal of the Acoustical Society of America, Oct 1, 2016
Non-negative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative ma... more Non-negative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative matrices. In the field of acoustics, multichannel expansion has been proposed to consider spatial information for sound source separation. Conventional multi-channel NMF has a difficulty in an initial-value dependency of the separation performance due to local minima. This paper proposes initial value settings by using binary masking based sound source separation whose masks on the time frequency domain are calculated from the time difference of arrival of each source. The proposed method calculates initial spatial correlation matrices using separated sources by binary masking. The music separation experiments confirmed that the separation performance of the proposed method was better than that of the conventional method. In addition, we evaluated initial value settings by using binary masking for automatic speech recognition (ASR) tasks in noisy environments. The ASR experiments confirmed that appropriate initial value settings were effective because initializations starting from previously well-estimated spatial correlation matrices achieved better ASR performances than random initializations.
Privacy preservation satisfying utility requirements based on multi-objective optimization
2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), Nov 29, 2022
We developed a dialogue system for Dialogue Robot Competition 2022. Our system is composed of thr... more We developed a dialogue system for Dialogue Robot Competition 2022. Our system is composed of three parts. First part investigates participants' demographic information by rule-based interview. Second part recommends a point of interest (POI) based on the collected demographic information. Third part answers participants' question based on the combination of rule-based answering and deep-learningbased answering with nearby POI search.
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Papers by Yuuki Tachioka