activity
20192026
most citedBYOL for Audio: Exploring Pre-trained General-purpose Audio Representations

59 citations · 194 across the 35 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

eess.AS2020★ 21 cited

Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval

Yuma Koizumi, Yasunori Ohishi, Daisuke Niizumi +2

The goal of audio captioning is to translate input audio into its description using natural language. One of the problems in audio captioning is the lack of training data due to th…

eess.AS2020★ 11 cited

Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning

Daiki Takeuchi, Yuma Koizumi, Yasunori Ohishi +2

The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, d…

eess.AS2020★ 21 cited

The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation

Yuma Koizumi, Daiki Takeuchi, Yasunori Ohishi +2

This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captionin…

eess.AS2020★ 8 cited

Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention

Yuma Koizumi, Kohei Yatabe, Marc Delcroix +2

This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly fro…

eess.AS2020★ 5 cited

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…