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20162025
most citedStatistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization

137 citations · 249 across the 12 of their papers we have counts for

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cs.SD2017★ 137 cited

Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization

Yoshiaki Bando, Masato Mimura, Katsutoshi Itoyama +2

This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach…

cs.AI2017

Generative Statistical Models with Self-Emergent Grammar of Chord Sequences

Hiroaki Tsushima, Eita Nakamura, Katsutoshi Itoyama +1

Generative statistical models of chord sequences play crucial roles in music processing. To capture syntactic similarities among certain chords (e.g. in C major key, between G and…