Author(s)

Ibrahim Aljubayri

Department of Computer Science and Information, Taibah University, Madinah, Saudi Arabia.

Environmental sound classification (ESC) has gained increasing attention in recent years. This study focuses on the evaluation of the popular public dataset Urbansound8k (Us8k) at different sampling rates using hand crafted features. The Us8k dataset contains environment sounds recorded at various sampling rates, and previous ESC works have uniformly resampled the dataset. Some previous work converted this data to different sampling rates for various reasons. Some of them chose to convert the rest of the dataset to 44,100, as the majority of the Us8k files were already at that sampling rate. On the other hand, some researchers down sampled the dataset to 8000, as it reduced computational complexity, while others resampled it to 16,000, aiming to achieve a balance between higher classification accuracy and lower computational complexity. In this research, we assessed the performance of ESC tasks using sampling rates of 8000 Hz, 16,000 Hz, and 44,100 Hz by extracting the hand crafted features Mel frequency cepstral coefficient (MFCC), gamma tone cepstral coefficients (GTCC), and Mel Spectrogram (MelSpec). The results indicated that there was no significant difference in the classification accuracy among the three tested sampling rates.

Deep Learning, Convolutional Neural Network, Environmental Sound Classification

Aljubayri, I. (2023) Comparative Analysis of Different Sampling Rates on Environmental Sound Classification Using the Urbansound8k Dataset. Journal of Computer and Communications, 11, 19-27. doi: 10.4236/jcc.2023.116002.