12 citations · 38 across the 13 of their papers we have counts for
13 papers
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…
Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
Kento Nagatomo, Masahiro Yasuda, Kohei Yatabe +2
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with t…
APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization
Tomoro Tanaka, Kohei Yatabe, Masahiro Yasuda +1
In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been de…
Sparse time-frequency representation via atomic norm minimization
Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa
Nonstationary signals are commonly analyzed and processed in the time-frequency (T-F) domain that is obtained by the discrete Gabor transform (DGT). The T-F representation obtained…
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…
Real-time speech enhancement using equilibriated RNN
Daiki Takeuchi, Kohei Yatabe, Yuma Koizumi +2
We propose a speech enhancement method using a causal deep neural network~(DNN) for real-time applications. DNN has been widely used for estimating a time-frequency~(T-F) mask whic…
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…
Invertible DNN-based nonlinear time-frequency transform for speech enhancement
Daiki Takeuchi, Kohei Yatabe, Yuma Koizumi +2
We propose an end-to-end speech enhancement method with trainable time-frequency~(T-F) transform based on invertible deep neural network~(DNN). The resent development of speech enh…
Data-driven design of perfect reconstruction filterbank for DNN-based sound source enhancement
Daiki Takeuchi, Kohei Yatabe, Yuma Koizumi +2
We propose a data-driven design method of perfect-reconstruction filterbank (PRFB) for sound-source enhancement (SSE) based on deep neural network (DNN). DNNs have been used to est…
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…