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Robust technique for environmental sound classification using convolutional recurrent neural network

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Abstract

Environmental sound classification(ESC) is one of the challenging issues in the area of audio recognition. Classifying the sounds such as glass breaking, gunshots, dog barking can aid in a variety of applications like audio surveillance, smart homes, robotic navigation, and other crime investigation systems. Environmental sounds are more complicated as compared to speech and music due to their unstructured nature. Environ- mental sounds are classified using machine learning algorithms such as Support Vector Machines, K-Nearest Neighbour, and various deep learn- ing algorithms such as Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory Neural Networks. In this paper, a robust approach is proposed for ESC on the UrbanSound8k dataset. Cep- stral features Mel Frequency Cepstral Coefficients are extracted from the audios and fed to the Convolutional Recurrent Neural Network (CRNN). The effect of varying various hyperparameters such as the number of layers, batch size, and filter count are analyzed to determine the ade- quate combination of parameters that can give considerable accuracy for ESC. The proposed approach is compared to the baseline models and it attains the best accuracy of 93.58% which is higher than the previ- ous research results of ESC. The proposed model gives the best results using one LSTM layer, 0.3 momentum value, 512 number of filters, 512 neurons in LSTM layer and 256 batch size. The proposed framework can be ameliorated by using novel neural networks and combinations.

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Data Availability

Data is available online at link “https://urbansounddataset.weebly.com/urbansound8k.html”

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Authors and Affiliations

  1. Computer Science and Engineering, GZS Campus College of Engineering and Technology, Maharaja Ranjit Singh Punjab Technical University, Bathinda, 151001, Punjab, India

    Anam Bansal & Naresh Kumar Garg

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  1. Anam Bansal
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  2. Naresh Kumar Garg
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Contributions

Anam Bansal: Conceptualization, Methodology, Software, Validation, Data curation, Visualization, Investigation, Writing- Reviewing and Editing.

Naresh Kumar Garg: Supervision, Quality Check, Reviewing, and Editing.

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Correspondence to Anam Bansal.

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The publicly available standard dataset UrbanSound8K is used.

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Bansal, A., Garg, N.K. Robust technique for environmental sound classification using convolutional recurrent neural network. Multimed Tools Appl 83, 54755–54772 (2024). https://doi.org/10.1007/s11042-023-17066-2

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