An Expert System for Identification of Domestic Emergency based on Normal and Abnormal Sound

Shilaskar, Swati; Bhatlawande, Shripad; Vaishale, Aditya; Duddalwar, Prapti; Ingale, Aditya

doi:10.1109/sictim56495.2023.10105052

2023 Somaiya International Conference on Technology and Information Management (SICTIM) 2023

DOI: 10.1109/sictim56495.2023.10105052

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An Expert System for Identification of Domestic Emergency based on Normal and Abnormal Sound

Swati Shilaskar¹,

Shripad Bhatlawande²,

Aditya Vaishale³

et al.

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2024

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Cited by 1 publication

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Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Song,

Xiong,

Wang

et al. 2024

Applied Sciences

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Indoor abnormal sound event identification refers to the automatic detection and recognition of abnormal sounds in an indoor environment using computer auditory technology. However, the process of model training usually requires a large amount of high-quality data, which can be time-consuming and costly to collect. Utilizing limited data has become another preferred approach for such research, but it introduces overfitting issues for machine learning models on small datasets. To overcome this issue, we proposed and validated the framework of combining the offline augmentation of raw audio and online augmentation of spectral features, making the application of small datasets in indoor anomalous sound event identification more feasible. Along with this, an improved two-dimensional audio convolutional neural network (EANN) was also proposed to evaluate and compare the impacts of different data augmentation methods under the framework on the sensitivity of sound event identification. Moreover, we further investigated the performance of four combinations of data augmentation techniques. Our research shows that the proposed combined data augmentation method has an accuracy of 97.4% on the test dataset, which is 10.6% higher than the baseline method. This demonstrates the method’s potential in the identification of indoor abnormal sound events.

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Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Song,

Xiong,

Wang

et al. 2024

Applied Sciences

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show abstract

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An Expert System for Identification of Domestic Emergency based on Normal and Abnormal Sound

Cited by 1 publication

References 19 publications

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

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