12 citations · 56 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…