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Daisuke Niizumi

3 accepted papers

2025

Collision-less and Balanced Sampling for Language-Queried Audio Source Separation

ICASSP 2025accepted

Language-queried audio source separation (LASS) is an emerging research field that has recently received increasing attention. This task aims to isolate individual sources from a mixture of signals using natural language descriptions, enabling applications in various areas such as automatic audio ed…

Cited by 0SourceScholar
2025

SoundBeam meets M2D: Target Sound Extraction with Audio Foundation Model

ICASSP 2025accepted

Target sound extraction (TSE) consists of isolating a desired sound from a mixture of arbitrary sounds using clues to identify it. A TSE system requires solving two problems at once, identifying the target source and extracting the target signal from the mixture. For increased practicability, the sa…

Cited by 0SourceScholar
2023

Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input

ICASSP 2023accepted

Masked Autoencoders is a simple yet powerful self-supervised learning method. However, it learns representations indirectly by reconstructing masked input patches. Several methods learn representations directly by predicting representations of masked patches; however, we think using all patches to e…

Cited by 0SourceScholar