An Efficient Anomalous Sound Detection System for Microcontrollers [PDF]
SensorsAnomalous Sound Detection (ASD) systems are pivotal in the Industrial Internet of Things (IIoT). Through the early detection of machines’ anomalies, these systems facilitate proactive maintenance, thereby mitigating potential losses.
Yi-Cheng Lo +3 more
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Acoustic-Sensing-Based Attribute-Driven Imbalanced Compensation for Anomalous Sound Detection without Machine Identity [PDF]
Sensors, 2023 Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal ...
Yifan Zhou, Yanhua Long, Haoran Wei
doaj +4 more sources
A Machine Anomalous Sound Detection Method Using the lMS Spectrogram and ES-MobileNetV3 Network
Applied Sciences, 2023 Unsupervised anomalous sound detection by machines holds significant importance within the realm of industrial automation. Currently, the task of machine-based anomalous sound detection in complex industrial settings is faced with issues such as the ...
Mei Wang +7 more
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Applied Sciences, 2021 The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data.
Yaoguang Wang +6 more
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Machine Anomalous Sound Detection Method Based on Lightweight Temporal Pyramid and ECA-MobileFaceNet [PDF]
SensorsTo address the challenges of scarce anomaly samples, inadequate modeling of temporal dynamic features, and limited feature selection capability of lightweight models in industrial anomalous sound detection, this paper proposes a method under an ...
Yuezhou Wu +3 more
doaj +2 more sources
Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection
EURASIP Journal on Audio, Speech, and Music Processing, 2023 Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods.
Jian Guan +6 more
doaj +2 more sources
Distributed Collaborative Anomalous Sound Detection by Embedding Sharing
2024 32nd European Signal Processing Conference (EUSIPCO)To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn an anomalous sound detection model while keeping their raw data private from each other.
Kota Dohi, Yohei Kawaguchi
exaly +5 more sources
Research Trends in Environmental Sound Analysis and Anomalous Sound Detection
Ieice Ess Fundamentals Review, 2022 Keisuke Imoto, Yohei Kawaguchi
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Anomalous Sound Detection Based on Sound Separation
INTERSPEECH 2023, 2023 This paper proposes an unsupervised anomalous sound detection method using sound separation. In factory environments, background noise and non-objective sounds obscure desired machine sounds, making it challenging to detect anomalous sounds. Therefore, using sounds not mixed with background noise or non-purpose sounds in the detection system is ...
Kanta Shimonishi +2 more
openaire +4 more sources
Anomalous Sound Detection Based on Machine Activity Detection
2022 30th European Signal Processing Conference (EUSIPCO), 2022 5 pages, 2 figures, 1 ...
Tomoya Nishida +4 more
openaire +3 more sources