59 citations · 181 across the 29 of their papers we have counts for
29 papers
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…
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…
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…
Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Masahiro Yasuda, Noboru Harada, Yasunori Ohishi +3
Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as 'events' in this paper) in complex and extensive real-worl…
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…
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…
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…
ConceptBeam: Concept Driven Target Speech Extraction
Yasunori Ohishi, Marc Delcroix, Tsubasa Ochiai +6
We propose a novel framework for target speech extraction based on semantic information, called ConceptBeam. Target speech extraction means extracting the speech of a target speake…
Introducing Auxiliary Text Query-modifier to Content-based Audio Retrieval
Daiki Takeuchi, Yasunori Ohishi, Daisuke Niizumi +2
The amount of audio data available on public websites is growing rapidly, and an efficient mechanism for accessing the desired data is necessary. We propose a content-based audio r…
Composing General Audio Representation by Fusing Multilayer Features of a Pre-trained Model
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
Many application studies rely on audio DNN models pre-trained on a large-scale dataset as essential feature extractors, and they extract features from the last layers. In this stud…
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
Recent general-purpose audio representations show state-of-the-art performance on various audio tasks. These representations are pre-trained by self-supervised learning methods tha…
BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
Pre-trained models are essential as feature extractors in modern machine learning systems in various domains. In this study, we hypothesize that representations effective for gener…
SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning
Marc Delcroix, Jorge Bennasar Vázquez, Tsubasa Ochiai +3
In many situations, we would like to hear desired sound events (SEs) while being able to ignore interference. Target sound extraction (TSE) tackles this problem by estimating the a…
Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion
Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito +1
We tackle a challenging task: multi-view and multi-modal event detection that detects events in a wide-range real environment by utilizing data from distributed cameras and microph…
Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments
Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito
Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments. A SELD system trained on known environment data is degrad…
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
Noboru Harada, Daisuke Niizumi, Daiki Takeuchi +3
This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS). As did for our previous ToyADMOS dataset, we collected a…