Anomalous Sound Detection Using Deep Audio Representation and a BLSTM Network for Audio Surveillance of Roads

Li, Yanxiong; Li, Xianku; Zhang, Yuhan; Liu, Mingle; Wang, Wucheng

doi:10.1109/access.2018.2872931

Cited by 45 publications

(20 citation statements)

References 48 publications

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“…Heavy traffic can lead to traffic accidents, which can be detected by the two sounds tire skidding and car crash [11], [12]. Another approach for traffic monitoring is to distinguish between vehicles in good and bad mechanical condition based on emitted sounds [2].…”

Section: Related Workmentioning

confidence: 99%

IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

Abeßer¹,

Gourishetti²,

Kátai³

et al. 2021

Preprint

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In many urban areas, traffic load and noise pollution are constantly increasing. Automated systems for traffic monitoring are promising countermeasures, which allow to systematically quantify and predict local traffic flow in order to to support municipal traffic planning decisions. In this paper, we present a novel open benchmark dataset, containing 2.5 hours of stereo audio recordings of 4718 vehicle passing events captured with both high-quality sE8 and medium-quality MEMS microphones. This dataset is well suited to evaluate the use-case of deploying audio classification algorithms to embedded sensor devices with restricted microphone quality and hardware processing power. In addition, this paper provides a detailed review of recent acoustic traffic monitoring (ATM) algorithms as well as the results of two benchmark experiments on vehicle type classification and direction of movement estimation using four state-of-the-art convolutional neural network architectures.

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Section: Related Workmentioning

confidence: 99%

IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

Abeßer¹,

Gourishetti²,

Kátai³

et al. 2021

Preprint

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“…The rest 20% of the audio recordings are used as test data. The detailed information concerning this audio corpus can be found online 1 . Two real-life audio corpora: the TUT Sound Events 2016 and the TUT Sound Events 2017 [5,20], were used in the experiments.…”

Section: Experimental Datamentioning

confidence: 99%

Sound Event Detection Via Dilated Convolutional Recurrent Neural Networks

Liu

Drossos

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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Convolutional recurrent neural networks (CRNNs) have achieved state-of-the-art performance for sound event detection (SED). In this paper, we propose to use a dilated CRNN, namely a CRNN with a dilated convolutional kernel, as the classifier for the task of SED. We investigate the effectiveness of dilation operations which provide a CRNN with expanded receptive fields to capture long temporal context without increasing the amount of CRNN's parameters. Compared to the classifier of the baseline CRNN, the classifier of the dilated CRNN obtains a maximum increase of 1.9%, 6.3% and 2.5% at F1 score and a maximum decrease of 1.7%, 4.1% and 3.9% at error rate (ER), on the publicly available audio corpora of the TUT-SED Synthetic 2016, the TUT Sound Event 2016 and the TUT Sound Event 2017, respectively.

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“…Over the past two years, there has been an emergence in research on the analysis of music through advanced ma-chine learning. This includes recent IEEE access publications studying the use of deep neural networks in the surveillance of roads from anomalous sounds [7], the generation of highfidelity audio samples through adversarial autoencoders [8], and new approaches of music genre classification [9], [10]. Although the audio analysis domain is well researched, this paper proposes a new topic of audio aesthetics for identification.…”

Section: Introductionmentioning

confidence: 99%

Person Identification From Audio Aesthetic

Sieu

Gavrilova

2021

IEEE Access

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Behavioral biometrics survey actions rather than the physical traits of the person. Within this categorization, social behavioral biometrics utilizes an individual's communications for biometric analysis. The investigation of the uniqueness of human preferences and their implications to other aspects of an individual, such as personality or gender, is both a psychological and a biometric problem. An emerging approach is the usage of an individual's aesthetic preferences for the purpose of person identification. Recent research into the identification from visual aesthetics has found that these preferences hold significant discriminatory value. However, aesthetic identification has only been conducted through a visual medium via a set of liked images. The contribution of this work is the development of the first audio aesthetic preference system for person identification. The proposed system extracts descriptive intra-song and intersong features from a set of songs favored by users and utilizes an ensemble of classifiers for prediction. The final decision is optimized by a genetic algorithm. Experimental results demonstrate that the developed audio aesthetic system achieves 95% user recognition accuracy on both proprietary and public audio datasets.INDEX TERMS audio aesthetics, behavioral biometrics, biometric security, human-machine interactions, pattern recognition

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Anomalous Sound Detection Using Deep Audio Representation and a BLSTM Network for Audio Surveillance of Roads

Cited by 45 publications

References 48 publications

IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

Sound Event Detection Via Dilated Convolutional Recurrent Neural Networks

Person Identification From Audio Aesthetic

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