59 citations · 171 across the 24 of their papers we have counts for
24 papers
Unsupervised Speech Recognition at the Syllable Level
Liming Wang, Kai-Wei Chang, Kunio Kashino +3
Training speech recognizers with unpaired speech and text -- known as unsupervised speech recognition (UASR) -- is a crucial step toward extending ASR to low-resource languages in…
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…
Deep Attentive Time Warping
Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan +4
Similarity measures for time series are important problems for time series classification. To handle the nonlinear time distortions, Dynamic Time Warping (DTW) has been widely used…
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…
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…
Reflectance-Oriented Probabilistic Equalization for Image Enhancement
Xiaomeng Wu, Yongqing Sun, Akisato Kimura +1
Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light ima…
Reflectance-Guided, Contrast-Accumulated Histogram Equalization
Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino
Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem,…
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…
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +2
Inspired by the recent progress in self-supervised learning for computer vision that generates supervision using data augmentations, we explore a new general-purpose audio represen…
Attention to Warp: Deep Metric Learning for Multivariate Time Series
Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan +4
Deep time series metric learning is challenging due to the difficult trade-off between temporal invariance to nonlinear distortion and discriminative power in identifying non-match…
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning
Daiki Takeuchi, Yuma Koizumi, Yasunori Ohishi +2
The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, d…
The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation
Yuma Koizumi, Daiki Takeuchi, Yasunori Ohishi +2
This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captionin…
Generative Adversarial Image Synthesis with Decision Tree Latent Controller
Takuhiro Kaneko, Kaoru Hiramatsu, Kunio Kashino
This paper proposes the decision tree latent controller generative adversarial network (DTLC-GAN), an extension of a GAN that can learn hierarchically interpretable representations…
Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition
Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered…
Understanding Community Structure in Layered Neural Networks
Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
A layered neural network is now one of the most common choices for the prediction of high-dimensional practical data sets, where the relationship between input and output data is c…
Modular Representation of Layered Neural Networks
Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatic…
A stochastic model of human visual attention with a dynamic Bayesian network
Akisato kimura, Derek Pang, Tatsuto Takeuchi +3
Recent studies in the field of human vision science suggest that the human responses to the stimuli on a visual display are non-deterministic. People may attend to different locati…
A quick search method for audio signals based on a piecewise linear representation of feature trajectories
Akisato Kimura, Kunio Kashino, Takayuki Kurozumi +1
This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves fe…