Audio Feature Extraction and Classification Technology Based on Convolutional Neural Network

Abstract

This study investigates the application of Convolutional Neural Networks (CNNs) in the domain of audio feature extraction and classification. Through systematic experimentation, diverse datasets spanning speech, music, and environmental sounds are utilized to train and evaluate CNN models. The statistical results demonstrate the efficacy of CNN-based approaches, with high accuracy, precision, recall, and F1-score achieved across various audio processing tasks, including speech recognition, music genre classification, and environmental sound monitoring. Comparative analysis against baseline models and alternative deep learning architectures reaffirms the superiority of CNNs, showcasing their ability to capture intricate patterns present in audio signals and overcome the limitations of traditional methods. Challenges such as dataset annotation, computational complexity, and robustness to noise are discussed, along with potential avenues for future research. Overall, this study contributes to the advancement of intelligent audio processing systems, highlighting the transformative potential of CNNs in unlocking new dimensions in auditory data analysis and interpretation.