most citedUniversal Score-based Speech Enhancement with High Content Preservation

2 citations · 2 across the 6 of their papers we have counts for

collaborators
Showing eess.ASShow all

3 papers · 1 filter

eess.AS2024

Song Data Cleansing for End-to-End Neural Singer Diarization Using Neural Analysis and Synthesis Framework

Hokuto Munakata, Ryo Terashima, Yusuke Fujita

We propose a data cleansing method that utilizes a neural analysis and synthesis (NANSY++) framework to train an end-to-end neural diarization model (EEND) for singer diarization.…

eess.AS2024

Audio Fingerprinting with Holographic Reduced Representations

Yusuke Fujita, Tatsuya Komatsu

This paper proposes an audio fingerprinting model with holographic reduced representation (HRR). The proposed method reduces the number of stored fingerprints, whereas conventional…

eess.AS2024★ 2 cited

Universal Score-based Speech Enhancement with High Content Preservation

Robin Scheibler, Yusuke Fujita, Yuma Shirahata +1

We propose UNIVERSE++, a universal speech enhancement method based on score-based diffusion and adversarial training. Specifically, we improve the existing UNIVERSE model that deco…