Identification of Sounds using Deep Learning with MFCC Features Extraction

Veeramanickam M.R.M.; Ingavale A.; Khullar V.; Tirth S.S.; Pandey B.; Singh H.P. · 2024 IEEE AITU: Digital Generation, Conference Proceedings - AITU 2024 (2024) · DOI: 10.1109/ieeeconf61558.2024.10585335

Resumen

Environmental sound classification is an essential aspect of identifying different classes of sounds using a machine learning approach. This work investigates the efficacy of convolutional neural networks for classifying short environmental sound clips. In this work, a deep 1D-CNN architecture with 2 layers of convolutional employing max-pooling, followed by two fully-connected layers are utilized. The model leverages segmented spectrograms with delta features as input. Evaluated with datasets encompassing environmental and urban recordings, our model surpasses baseline methods utilizing mel-frequency cepstral coefficients (MFCCs) and achieves competitive performance against traditional approaches.

Instituciones: Universidad Nacional Mayor de San Marcos