143 citations · 527 across the 38 of their papers we have counts for
38 papers
ReverbMiipher: Generative Speech Restoration meets Reverberation Characteristics Controllability
Wataru Nakata, Yuma Koizumi, Shigeki Karita +5
Reverberation encodes spatial information regarding the acoustic source environment, yet traditional Speech Restoration (SR) usually completely removes reverberation. We propose Re…
Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration
Shigeki Karita, Yuma Koizumi, Heiga Zen +3
Training data cleaning is a new application for generative model-based speech restoration (SR). This paper introduces Miipher-2, an SR model designed for million-hour scale data, f…
FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks
Min Ma, Yuma Koizumi, Shigeki Karita +4
This paper introduces FLEURS-R, a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus. FLEURS-R maintains…
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
Yuma Koizumi, Heiga Zen, Shigeki Karita +7
This paper introduces a new speech dataset called ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is derived by applying speech restoration to the LibriTTS corpus, which c…
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…
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Yuma Koizumi, Heiga Zen, Shigeki Karita +7
Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a n…
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration
Yuma Koizumi, Kohei Yatabe, Heiga Zen +1
Denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs) are popular generative models for neural vocoders. The DDPMs and GANs can be characteriz…
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…
Mask scalar prediction for improving robust automatic speech recognition
Arun Narayanan, James Walker, Sankaran Panchapagesan +2
Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints a…
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping
Yuma Koizumi, Heiga Zen, Kohei Yatabe +2
Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features. In this study, w…
SNRi Target Training for Joint Speech Enhancement and Recognition
Yuma Koizumi, Shigeki Karita, Arun Narayanan +2
Speech enhancement (SE) is used as a frontend in speech applications including automatic speech recognition (ASR) and telecommunication. A difficulty in using the SE frontend is th…
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
Yuma Koizumi, Shigeki Karita, Scott Wisdom +4
Single-channel speech enhancement (SE) is an important task in speech processing. A widely used framework combines an analysis/synthesis filterbank with a mask prediction network,…
Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions
Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi +6
We present the task description and discussion on the results of the DCASE 2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound detection (ASD) task, identi…
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method
Koichi Saito, Tomohiko Nakamura, Kohei Yatabe +2
Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the v…
Noisy-target Training: A Training Strategy for DNN-based Speech Enhancement without Clean Speech
Takuya Fujimura, Yuma Koizumi, Kohei Yatabe +1
Deep neural network (DNN)-based speech enhancement ordinarily requires clean speech signals as the training target. However, collecting clean signals is very costly because they mu…
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval
Yuma Koizumi, Yasunori Ohishi, Daisuke Niizumi +2
The goal of audio captioning is to translate input audio into its description using natural language. One of the problems in audio captioning is the lack of training data due to th…
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…
A Transformer-based Audio Captioning Model with Keyword Estimation
Yuma Koizumi, Ryo Masumura, Kyosuke Nishida +2
One of the problems with automated audio captioning (AAC) is the indeterminacy in word selection corresponding to the audio event/scene. Since one acoustic event/scene can be descr…
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…
Listen to What You Want: Neural Network-based Universal Sound Selector
Tsubasa Ochiai, Marc Delcroix, Yuma Koizumi +3
Being able to control the acoustic events (AEs) to which we want to listen would allow the development of more controllable hearable devices. This paper addresses the AE sound sele…
Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto +8
In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitori…
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation
Masahiro Yasuda, Yuma Koizumi, Shoichiro Saito +2
We propose a direction-of-arrival (DOA) estimation method for Sound Event Localization and Detection (SELD). Direct estimation of DOA using a deep neural network (DNN), i.e. comple…
Stable Training of DNN for Speech Enhancement based on Perceptually-Motivated Black-Box Cost Function
Masaki Kawanaka, Yuma Koizumi, Ryoichi Miyazaki +1
Improving subjective sound quality of enhanced signals is one of the most important missions in speech enhancement. For evaluating the subjective quality, several methods related t…
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention
Yuma Koizumi, Kohei Yatabe, Marc Delcroix +2
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly fro…