Feature Extraction and Enhanced Classification of Urban Sounds
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Electronics & Communication Engineering | India | Volume 12 Issue 9, September 2023 | Popularity: 5.3 / 10


     

Feature Extraction and Enhanced Classification of Urban Sounds

Asma Begum, Afshaan Kaleem


Abstract: Urban Sound Classification is an important but challenging problem. In this paper, we present a new deep convolutional neural network for classification tasks that combines MFCC with Mel spectrogram. In comparison to using a single feature, this feature combination can make the features richer. The network suggested extracts and derives high-level features using three convolutional blocks, each of which is made up of two convolutional layers and a pooling layer. We apply a filter with a limited receptive field in each convolutional layer to preserve the network's depth and lower the number of parameters. On ESC-50 and UrbanSound8K, where our technique was tested, classification accuracy was 45.60% and 91.0%, respectively. The experimental results show that the proposed method is suitable for Urban Sound classification


Keywords: MFCC, Feature Extraction. Deep learning, Urban Sound classification


Edition: Volume 12 Issue 9, September 2023


Pages: 1461 - 1464


DOI: https://www.doi.org/10.21275/SR23918200525


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Asma Begum, Afshaan Kaleem, "Feature Extraction and Enhanced Classification of Urban Sounds", International Journal of Science and Research (IJSR), Volume 12 Issue 9, September 2023, pp. 1461-1464, https://www.ijsr.net/getabstract.php?paperid=SR23918200525, DOI: https://www.doi.org/10.21275/SR23918200525

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