Yuma Koizumi

Yuma Koizumi

Yuma Koizumi is a research scientist at Google Research. He received his B.S. and M.S degrees in 2012 and 2014 from Hosei University, Tokyo, respectively, and his Ph.D. degree in 2017 from the University of Electro-Communications, Tokyo. He was with the NTT Media Intelligence Laboratories at Nippon Telegraph and Telephone (NTT), Tokyo between 2014 and 2020. His current research interests are speech enhancement, environmental sound analysis, and automatic speech recognition.
Authored Publications
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    Preview abstract 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 consists of 585 hours of speech data at 24 kHz sampling rate from 2,456 speakers and the corresponding texts. The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved. Experimental results show that the LibriTTS-R ground-truth samples showed significantly improved sound quality compared to those in LibriTTS. In addition, neural end-to-end TTS trained with LibriTTS-R achieved speech naturalness on par with that of the ground-truth samples. The corpus is freely available for download from [URL-HERE] View details
    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
    Daisuke Niizumi
    Tomoya Nishida
    Harsh Purohit
    Ryo Tanabe
    Takashi Endo
    Yohei Kawaguchi
    DCASE 2023(2023) (to appear)
    Preview abstract 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 (ASD) for machine condition monitoring". The main goal is to enable rapid deployment of ASD systems for new kinds of machines using only a few normal samples, without the need for hyperparameter tuning. In the past ASD tasks, developed methods tuned hyperparameters for each machine type, as the development and evaluation datasets had the same machine types. However, collecting normal and anomalous data as the development dataset can be infeasible in practice. In 2023 Task 2, we focus on solving first-shot problem, which is the challenge of training a model on a few machines of a completely novel machine type. Specifically, (i) each machine type has only one section, and (ii) machine types in the development and evaluation datasets are completely different. We will add challenge results and analysis of the submissions after the challenge submission deadline. View details
    Preview abstract 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 new SR application: increasing the amount of high-quality training data for speech generation by converting speech samples collected from the web to studio-quality. To make our SR model robust against various degradation, we use (i) a speech representation extracted from w2v-BERT for the input feature, and (ii) linguistic features extracted from transcripts and PnG-BERT for conditioning features. Experiments show that the proposed model (i) is robust against various audio degradation, (ii) can restore samples in the LJspeech dataset and improves the quality of text-to-speech (TTS) outputs without changing the model and hyper-parameters, and (iii) enable us to train a high-quality TTS model from restored speech samples collected from the web. View details
    Preview abstract 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 that the appropriate noise reduction level differs depending on applications and/or noise characteristics. In this study, we propose ``{\it signal-to-noise ratio improvement (SNRi) target training}''; the SE frontend is trained to output a signal whose SNRi is controlled by an auxiliary scalar input. In joint training with a backend, the target SNRi value is estimated by an auxiliary network. By training all networks to minimize the backend task loss, we can estimate the appropriate noise reduction level for each noisy input in a data-driven scheme. Our experiments showed that the SNRi target training enables control of the output SNRi. In addition, the proposed joint training relatively reduces word error rate by 4.0\% and 5.7\% compared to a Conformer-based standard ASR model and conventional SE-ASR joint training model, respectively. Furthermore, by analyzing the predicted target SNRi, we observed the jointly trained network automatically controls the target SNRi according to noise characteristics. Audio demos are available in our demo page [google.github.io/df-conformer/snri_target/]. View details
    Preview abstract Denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs) are popular generative models for neural vocoders. The DDPMs and GANs can be characterized by the iterative denoising framework and adversarial training, respectively. This study proposes a fast and high-quality neural vocoder called WaveFit, which integrates the essence of GANs into a DDPM-like iterative framework based on fixed-point iteration. WaveFit iteratively denoises an input signal, and trains a deep neural network (DNN) for minimizing an adversarial loss calculated from intermediate outputs at all iterations. Subjective (side-by-side) listening tests showed no statistically significant differences in naturalness between human natural speech and those synthesized by WaveFit with five iterations. Furthermore, the inference speed of WaveFit was more than 240 times faster than WaveRNN. Audio demos are available at google.github.io/df-conformer/wavefit/. View details
    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
    Daisuke Niizumi
    Tomoya Nishida
    Harsh Purohit
    Takashi Endo
    Masaaki Yamamoto
    Yohei Kawaguchi
    DCASE 2022 Workshop(2022)
    Preview abstract We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: “Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques”. Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. We will add challenge results and analysis of the submissions after the challenge submission deadline. View details
    Preview abstract 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, we propose SpecGrad that adapts the diffusion noise so that its time-varying spectral envelope becomes close to the conditioning log-mel spectrogram. This adaptation by time-varying filtering improves the sound quality especially in the high-frequency bands. It is processed in the time-frequency domain to keep the computational cost almost the same as the conventional DDPM-based neural vocoders. Experimental results showed that SpecGrad generates higher-fidelity speech waveform than conventional DDPM-based neural vocoders in both analysis-synthesis and speech enhancement scenarios. Audio demos are available at [wavegrad.github.io/specgrad/]. View details
    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
    Noboru Harada
    Daisuke Niizumi
    Kota Dohi
    Ryo Tanabe
    Harsh Purohit
    Takashi Endo
    Proceedings of Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE)(2021) (to appear)
    Preview abstract 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, identifying whether a given sound was normal or anomalous without anomalous training data. In 2021, we organized an advanced unsupervised ASD task under domain-shift conditions, which focuses on the inevitable problem of the practical use of ASD systems. The main challenge of this task is to detect unknown anomalous sounds where the acoustic characteristics of the training and testing samples are different, i.e., domain-shifted. This problem frequently occurs due to changes in seasons, manufactured products, and/or environmental noise. We received 75 submissions from 26 teams, and several novel approaches have been developed in this challenge. On the basis of the analysis of the evaluation results, we found that there are two types of remarkable approaches that TOP-5 winning teams adopted: 1) ensemble approaches of "outlier exposure" (OE)-based detectors and "inlier modeling" (IM)-based detectors and 2) approaches based on IM-based detection for features learned in a machine-identification task. View details
    Preview abstract Combinations of a trainable filterbank and a mask prediction network is a strong framework in single-channel speech enhancement (SE). Since the denoising performance and computational efficiency are mainly affected by the structure of the mask prediction network, we aim to improve this network. In this study, by focusing on a similarity between the structure of Conv-TasNet and Conformer, we integrate the Conformer into SE as a mask prediction network to benefit its powerful sequential modeling ability. To improve the computational complexity and local sequential modeling, we extend the Conformer using linear complexity attention and stacked 1-D dilated depthwise convolution layers. Experimental results show that (i) the use of linear complexity attention avoids high computational complexity, and (ii) our model achieves higher scale-invariant signal-to-noise ratio than the improved time-dilated convolution network (TDCN++), an extended version of Conv-TasNet. View details