Keigo Wakayama — accepted AI-conference papers

2024

Online Target Sound Extraction with Knowledge Distillation from Partially Non-Causal Teacher

Keigo Wakayama, Tsubasa Ochiai, Marc Delcroix, Masahiro Yasuda, Shoichiro Saito, Shoko Araki +1

ICASSP 2024accepted

Target Sound Extraction (TSE) is a technique for extracting sound events belonging to a target sound class in a mixture using a Deep Neural Network (DNN). Offline TSE that uses non-causal models has achieved high extraction performance. However, many applications require online processing. Simply co…

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2022

CNN-Transformer with Self-Attention Network for Sound Event Detection

Keigo Wakayama, Shoichiro Saito

ICASSP 2022accepted

In sound event detection (SED), the representation ability of deep neural network (DNN) models must be increased to significantly improve the accuracy or increase the number of classifiable classes. When building large-scale DNN models, a highly parameter-efficient DNN architecture should preferably…

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