10 citations · 19 across the 24 of their papers we have counts for
24 papers
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…
Description and Discussion on DCASE 2026 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Binh Thien Nguyen, Masahiro Yasuda, Noboru Harada +8
This paper presents an overview of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2026 Challenge Task 4, Spatial Semantic Segmentation of Sound Scenes (S5).…
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…
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…
FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters
Hiro Ishii, Kenta Niwa, Hiroshi Sawada +3
We propose Federated Preconditioned Mixing (FedPM), a novel Federated Learning (FL) method that leverages second-order optimization. Prior methods--such as LocalNewton, LTDA, and F…
Learning to assess subjective impressions from speech
Yuto Kondo, Hirokazu Kameoka, Kou Tanaka +2
We tackle a new task of training neural network models that can assess subjective impressions conveyed through speech and assign scores accordingly, inspired by the work on automat…
Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Masahiro Yasuda, Binh Thien Nguyen, Noboru Harada +10
Spatial Semantic Segmentation of Sound Scenes (S5) aims to enhance technologies for sound event detection and separation from multi-channel input signals that mix multiple sound ev…
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…
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer
Daiki Takeuchi, Binh Thien Nguyen, Masahiro Yasuda +3
Automated Audio Captioning (AAC) aims to describe the semantic contexts of general sounds, including acoustic events and scenes, by leveraging effective acoustic features. To enhan…
Towards Pre-training an Effective Respiratory Audio Foundation Model
Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3
Recent advancements in foundation models have sparked interest in respiratory audio foundation models. However, the effectiveness of applying conventional pre-training schemes to d…
Assessing the Utility of Audio Foundation Models for Heart and Respiratory Sound Analysis
Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3
Pre-trained deep learning models, known as foundation models, have become essential building blocks in machine learning domains such as natural language processing and image domain…
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…
Baseline Systems and Evaluation Metrics for Spatial Semantic Segmentation of Sound Scenes
Binh Thien Nguyen, Masahiro Yasuda, Daiki Takeuchi +3
Immersive communication has made significant advancements, especially with the release of the codec for Immersive Voice and Audio Services. Aiming at its further realization, the D…
Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi +9
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot unsupervised anomalous sound detection (…
M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +4
Contrastive language-audio pre-training (CLAP) enables zero-shot (ZS) inference of audio and exhibits promising performance in several classification tasks. However, conventional a…
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
To reduce the need for skilled clinicians in heart sound interpretation, recent studies on automating cardiac auscultation have explored deep learning approaches. However, despite…
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
Self-supervised learning (SSL) using masked prediction has made great strides in general-purpose audio representation. This study proposes Masked Modeling Duo (M2D), an improved ma…
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval
Shunsuke Tsubaki, Daisuke Niizumi, Daiki Takeuchi +3
The aim of this research is to refine knowledge transfer on audio-image temporal agreement for audio-text cross retrieval. To address the limited availability of paired non-speech…
Unrestricted Global Phase Bias-Aware Single-channel Speech Enhancement with Conformer-based Metric GAN
Shiqi Zhang, Zheng Qiu, Daiki Takeuchi +2
With the rapid development of neural networks in recent years, the ability of various networks to enhance the magnitude spectrum of noisy speech in the single-channel speech enhanc…
Audio Difference Captioning Utilizing Similarity-Discrepancy Disentanglement
Daiki Takeuchi, Yasunori Ohishi, Daisuke Niizumi +2
We proposed Audio Difference Captioning (ADC) as a new extension task of audio captioning for describing the semantic differences between input pairs of similar but slightly differ…
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
Self-supervised learning general-purpose audio representations have demonstrated high performance in a variety of tasks. Although they can be optimized for application by fine-tuni…
Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Kota Dohi, Keisuke Imoto, Noboru Harada +7
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: ``First-shot unsupervised anomalous sound detection…
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline
Noboru Harada, Daisuke Niizumi, Yasunori Ohishi +2
This paper provides a baseline system for First-shot-compliant unsupervised anomaly detection (ASD) for machine condition monitoring. First-shot ASD does not allow systems to do ma…
Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
Masked Autoencoders is a simple yet powerful self-supervised learning method. However, it learns representations indirectly by reconstructing masked input patches. Several methods…