collaborators

11 papers

eess.AS2026

Description and Discussion on DCASE 2026 Challenge Task 2: Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

Tomoya Nishida, Noboru Harada, Daiki Takeuchi +6

This paper presents an overview of DCASE 2026 Challenge Task 2, titled "Noise-aware unsupervised anomalous sound detection (UASD) for machine condition monitoring." The task aims t…

eess.AS2026

Rethinking Masking Strategies for Masked Prediction-based Audio Self-supervised Learning

Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3

Since the introduction of Masked Autoencoders, various improvements to masking techniques have been explored. In this paper, we rethink masking strategies for audio representation…

eess.AS2026

What Do Neurons Listen To? A Neuron-level Dissection of a General-purpose Audio Model

Takao Kawamura, Daisuke Niizumi, Nobutaka Ono

In this paper, we analyze the internal representations of a general-purpose audio self-supervised learning (SSL) model from a neuron-level perspective. Despite their strong empiric…

eess.AS2026

Class-Aware Permutation-Invariant Signal-to-Distortion Ratio for Semantic Segmentation of Sound Scene with Same-Class Sources

Binh Thien Nguyen, Masahiro Yasuda, Daiki Takeuchi +2

To advance immersive communication, the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge recently introduced Task 4 on Spatial Semantic Segmentatio…

cs.SD2025

Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

Tomoya Nishida, Noboru Harada, Daisuke Niizumi +9

This paper introduces the task description for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge Task 2, titled "First-shot unsupervised anomalo…

eess.AS2025

M2D-CLAP: Exploring General-purpose Audio-Language Representations Beyond CLAP

Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3

Contrastive language-audio pre-training (CLAP), which learns audio-language representations by aligning audio and text in a common feature space, has become popular for solving aud…