Výsledky vyhledávání - "YAMAMOTO, Masaaki"

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End-to-End Integration of Speech Emotion Recognition with Voice Activity Detection using Self-Supervised Learning Features

Autor: Yamashita, Natsuo, Yamamoto, Masaaki, Kawaguchi, Yohei

Speech Emotion Recognition (SER) often operates on speech segments detected by a Voice Activity Detection (VAD) model. However, VAD models may output flawed speech segments, especially in noisy environments, resulting in degraded performance of subse

Externí odkaz: http://arxiv.org/abs/2410.13282

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Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

Autor: Dohi, Kota, Imoto, Keisuke, Harada, Noboru, Niizumi, Daisuke, Koizumi, Yuma, Nishida, Tomoya, Purohit, Harsh, Endo, Takashi, Yamamoto, Masaaki, Kawaguchi, Yohei

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 applying domain generalization techniques''. Domain shifts are a critica

Externí odkaz: http://arxiv.org/abs/2206.05876

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Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection

Autor: Purohit, Harsh, Endo, Takashi, Yamamoto, Masaaki, Kawaguchi, Yohei

This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Existing approaches such as deep autoencoder (DAE), variational autoencoder (VAE), conditional variational autoencoder (CVAE)

Externí odkaz: http://arxiv.org/abs/2206.05460

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MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task

Autor: Dohi, Kota, Nishida, Tomoya, Purohit, Harsh, Tanabe, Ryo, Endo, Takashi, Yamamoto, Masaaki, Nikaido, Yuki, Kawaguchi, Yohei

We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD). Domain shifts are differences in data distributions that can degrade the detection performance, and handling them is a major issue f

Externí odkaz: http://arxiv.org/abs/2205.13879

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Anomalous Sound Detection Based on Machine Activity Detection

Autor: Nishida, Tomoya, Dohi, Kota, Endo, Takashi, Yamamoto, Masaaki, Kawaguchi, Yohei

We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task -- detecting when the target machine is active. First, we train a model that detects machine activity by using normal

Externí odkaz: http://arxiv.org/abs/2204.07353

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Environmental Sound Extraction Using Onomatopoeic Words

Autor: Okamoto, Yuki, Horiguchi, Shota, Yamamoto, Masaaki, Imoto, Keisuke, Kawaguchi, Yohei

An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We propose an environmental-sound-extraction method using onomatopoeic wo

Externí odkaz: http://arxiv.org/abs/2112.00209

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