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

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