Convolutional Neural Network-based Acoustic Scene Classification using Litis Rouen Dataset

The goal of acoustic scene classification (ASC) is to automatically classify the environment based on an audio recording of the situation. It has become a significant but difficult issue in audio processing, allowing for a vast variety of successive applications such as security, social care, and context-aware facilities. Recently, Convolutional Neural Networks (CNNs) have been broadly applied to speech audio classification, yielding promising results. In this paper, we have extracted four types of acoustic features i.e., Mel-Frequency Cepstral Coefficients (MFCCs), Linear Predictive Coding (LPC), Zero-crossing-rate (ZCR) and root mean square value (RMSV) value from audio signals for automatic scene classification. We have used Litis Rouen Dataset for CNN model training and testing. Experimental results show that the proposed method has achieved a significant accuracy of 84.15% on the Litis Rouen Dataset. Moreover, we have also compared the performance of the proposed method with other ASC techniques used in the literature.