12 citations · 38 across the 13 of their papers we have counts for
5 papers · 1 filter
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…