The growing usage of digital microphones has generated an increased interest in the topic of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application domains, including working machines and in-vehicle intelligence (the main target of this research project). This paper introduces three deep AutoEncoders (AE) for unsupervised AAD tasks, namely a Dense AE, a Convolutional Neural Network (CNN) AE and Long Short-Term Memory Autoencoder (LSTM) AE. To tune the deep learning architectures, development data were adopted from public domain audio datasets related with working machines. A large set of computational experiments was held, showing that the three proposed deep autoencoders, when combined with a melspectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Next, on a second experimental stage, aiming to address the final in-vehicle passenger safety goal, the three AEs were adapted to learn from in-vehicle normal audio, assuming three realistic scenarios that were generated by a synthetic audio mixture tool. In general, a high quality AAD discrimination was obtained: working machine data - 72% to 91%; and in-vehicle audio - 78% to 81%. In conjunction with an automotive company, an in-vehicle AAD intelligent system prototype was further developed, aiming to test a selected model (LSTM AE) during a pilot demonstration event that targeted the cough anomaly. Interesting results were obtained, with the AAD system presenting a high cough classification accuracy (e.g., 100% for front seat locations). ; This work is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) - Project no 039334; Funding Reference: POCI-01-0247-FEDER-039334.


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    Title :

    Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio



    Publication date :

    2022-05-19


    Remarks:

    doi:10.1007/s00521-022-07375-2



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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