143 citations · 511 across the 26 of their papers we have counts for
7 papers · 1 filter
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…
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
Kota Dohi, Keisuke Imoto, Noboru Harada +7
We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring ap…
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…