ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

Koizumi, Yohei; Saito, Shoichiro; Uematsu, Hitoshi; Harada, Noboru; Imoto, Keisuke

doi:10.1109/waspaa.2019.8937164

Cited by 121 publications

(64 citation statements)

References 21 publications

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“…Machines have been known to be monitored via the acquisition of certain sensor data: voltage and current [2], temperature and pressure [3], vibration [4,5,6,7,8,9,10] and sound [11,12,13,14,15,16,17]. Vibration and sound have been reported effective sensor signals to characterize a machine behavior.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Talmoudi

Kanada²,

Hirata

2020

Preprint

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One of the main focuses of smart industry is machinery failure predictive solutions. To achieve this, IoT-based solutions have been widely deployed. However, data processing and decision making remain challenging. The absence of enough knowledge has been the primarily limitation of statistical methods and supervised learning methods. Therefore, unsupervised learning methods are gaining more popularity but still have limits to cover effectively the pre-signs of failures due to the complexity of training process and results visualization. Previously, we proposed a novel Big Data Analysis method on audio/vibration data to cover effectively the pre-signs of failures through data visualization without complex learning or processing. We validated our proposal on a demo system. In the present work, we are using part of the MIMII dataset to test our proposed analysis method on a real-world-like data and verify the validity of our proposal on a more complex system. We are showing that we can detect abnormal machine behaviors and predict failures without prior training or knowledge of the target monitored machine.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Talmoudi

Kanada²,

Hirata

2020

Preprint

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“…Another interesting audio application of autoencoders is anomalous sound detection, which is the task of identifying whether a sound corresponds to a normal (known) or abnormal (unwanted) class [39,40]. The main challenge of this problem is to detect the anomaly having only training samples of normal behavior.…”

Section: Autoencoders In Audio Processing Tasksmentioning

confidence: 99%

Open Set Audio Classification Using Autoencoders Trained on Few Data

Naranjo-Alcazar

Perez-Castanos²,

Zuccarello³

et al. 2020

Sensors

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Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training. It can be summarized as the problem of correctly identifying instances from a known class (seen during training) while rejecting any unknown or unwanted samples (those belonging to unseen classes). Another problem arising in practical scenarios is few-shot learning (FSL), which appears when there is no availability of a large number of positive samples for training a recognition system. Taking these two limitations into account, a new dataset for OSR and FSL for audio data was recently released to promote research on solutions aimed at addressing both limitations. This paper proposes an audio OSR/FSL system divided into three steps: a high-level audio representation, feature embedding using two different autoencoder architectures and a multi-layer perceptron (MLP) trained on latent space representations to detect known classes and reject unwanted ones. An extensive set of experiments is carried out considering multiple combinations of openness factors (OSR condition) and number of shots (FSL condition), showing the validity of the proposed approach and confirming superior performance with respect to a baseline system based on transfer learning.

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“…The large amounts of data required to train deep learning models has motivated the creation of a number of datasets containing sounds from urban [27][28][29], domestic [30], industrial [31,32], and generic [15,[33][34][35] environments. Nevertheless, sounds for our application are scarce and difficult to produce, given the particular focus of our "Detect and Avoid" system: small aircraft flying at VLL and within two kilometers of the microphones.…”

Section: Dataset: Small Aircraft Soundsmentioning

confidence: 99%

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

Harana¹,

Alarcon²,

González³

et al. 2020

Preprint

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For the Remotely Piloted Aircraft Systems (RPAS) market to continue its current growth rate, cost-effective "Detect and Avoid" systems that enable safe beyond visual line of sight (BVLOS) operations are critical. We propose an audio-based "Detect and Avoid" system, composed of microphones and an embedded computer, which performs real-time inferences using a sound event detection (SED) deep learning model. Two state-of-the-art SED models, YAMNet and VGGish, are fine-tuned using our dataset of aircraft sounds and their performances are compared for a wide range of configurations. YAMNet, whose MobileNet architecture is designed for embedded applications, outperformed VGGish both in terms of aircraft detection and computational performance. YAMNet's optimal configuration, with > 70% true positive rate and precision, results from combining data augmentation and undersampling with the highest available inference frequency (i.e. 10 Hz). While our proposed "Detect and Avoid" system already allows the detection of small aircraft from sound in real time, additional testing using multiple aircraft types is required. Finally, a larger training dataset, sensor fusion, or remote computations on cloud-based services could further improve system performance.

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ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

Cited by 121 publications

References 21 publications

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Open Set Audio Classification Using Autoencoders Trained on Few Data

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

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