12 citations · 56 across the 20 of their papers we have counts for
20 papers
Mel-Spectrogram Inversion via Alternating Direction Method of Multipliers
Yoshiki Masuyama, Natsuki Ueno, Nobutaka Ono
Signal reconstruction from its mel-spectrogram is known as mel-spectrogram inversion and has many applications, including speech and foley sound synthesis. In this paper, we propos…
Signal Reconstruction from Mel-spectrogram Based on Bi-level Consistency of Full-band Magnitude and Phase
Yoshiki Masuyama, Natsuki Ueno, Nobutaka Ono
We propose an optimization-based method for reconstructing a time-domain signal from a low-dimensional spectral representation such as a mel-spectrogram. Phase reconstruction has b…
Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation
Yoshiki Masuyama, Xuankai Chang, Wangyou Zhang +5
Neural speech separation has made remarkable progress and its integration with automatic speech recognition (ASR) is an important direction towards realizing multi-speaker ASR. Thi…
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
Samuele Cornell, Matthew Wiesner, Shinji Watanabe +8
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR (DA…
Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation
Yoshiaki Bando, Yoshiki Masuyama, Aditya Arie Nugraha +1
This paper describes an efficient unsupervised learning method for a neural source separation model that utilizes a probabilistic generative model of observed multichannel mixtures…
Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge
Samuele Cornell, Zhong-Qiu Wang, Yoshiki Masuyama +3
This paper describes our submission to the Second Clarity Enhancement Challenge (CEC2), which consists of target speech enhancement for hearing-aid (HA) devices in noisy-reverberan…
Online Phase Reconstruction via DNN-based Phase Differences Estimation
Yoshiki Masuyama, Kohei Yatabe, Kento Nagatomo +1
This paper presents a two-stage online phase reconstruction framework using causal deep neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time…
End-to-End Integration of Speech Recognition, Dereverberation, Beamforming, and Self-Supervised Learning Representation
Yoshiki Masuyama, Xuankai Chang, Samuele Cornell +2
Self-supervised learning representation (SSLR) has demonstrated its significant effectiveness in automatic speech recognition (ASR), mainly with clean speech. Recent work pointed o…
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
Yen-Ju Lu, Xuankai Chang, Chenda Li +10
This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features hav…
Joint Optimization of Sampling Rate Offsets Based on Entire Signal Relationship Among Distributed Microphones
Yoshiki Masuyama, Kouei Yamaoka, Nobutaka Ono
In this paper, we propose to simultaneously estimate all the sampling rate offsets (SROs) of multiple devices. In a distributed microphone array, the SRO is inevitable, which deter…
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
Yoshiki Masuyama, Yoshiaki Bando, Kohei Yatabe +3
Detecting sound source objects within visual observation is important for autonomous robots to comprehend surrounding environments. Since sounding objects have a large variety with…
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention
Yuma Koizumi, Kohei Yatabe, Marc Delcroix +2
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly fro…
Phase reconstruction based on recurrent phase unwrapping with deep neural networks
Yoshiki Masuyama, Kohei Yatabe, Yuma Koizumi +2
Phase reconstruction, which estimates phase from a given amplitude spectrogram, is an active research field in acoustical signal processing with many applications including audio s…
Consistency-aware multi-channel speech enhancement using deep neural networks
Yoshiki Masuyama, Masahito Togami, Tatsuya Komatsu
This paper proposes a deep neural network (DNN)-based multi-channel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal.…
Unsupervised Training for Deep Speech Source Separation with Kullback-Leibler Divergence Based Probabilistic Loss Function
Masahito Togami, Yoshiki Masuyama, Tatsuya Komatsu +1
In this paper, we propose a multi-channel speech source separation with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an al…
Multichannel Loss Function for Supervised Speech Source Separation by Mask-based Beamforming
Yoshiki Masuyama, Masahito Togami, Tatsuya Komatsu
In this paper, we propose two mask-based beamforming methods using a deep neural network (DNN) trained by multichannel loss functions. Beamforming technique using time-frequency (T…
Low-rankness of Complex-valued Spectrogram and Its Application to Phase-aware Audio Processing
Yoshiki Masuyama, Kohei Yatabe, Yasuhiro Oikawa
Low-rankness of amplitude spectrograms has been effectively utilized in audio signal processing methods including non-negative matrix factorization. However, such methods have a fu…
Phase-aware Harmonic/Percussive Source Separation via Convex Optimization
Yoshiki Masuyama, Kohei Yatabe, Yasuhiro Oikawa
Decomposition of an audio mixture into harmonic and percussive components, namely harmonic/percussive source separation (HPSS), is a useful pre-processing tool for many audio appli…
Deep Griffin-Lim Iteration
Yoshiki Masuyama, Kohei Yatabe, Yuma Koizumi +2
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN). T…
Designing nearly tight window for improving time-frequency masking
Tsubasa Kusano, Yoshiki Masuyama, Kohei Yatabe +1
Many audio signal processing methods are formulated in the time-frequency (T-F) domain which is obtained by the short-time Fourier transform (STFT). The properties of the STFT are…