Automatic Music Classification Algorithm based on AI Internet and Image Processing Technology

Automatic music classification algorithms have become a research hotspot in the field of music information retrieval. This study uses deep learning to denoise and normalize audio signals, and then uses Mel-frequency cepstral coefficients (MFCC) and image texture, color and other feature extraction techniques to establish rich feature representations for music works. On this basis, a music classification model is constructed using a long short-term memory network (LSTM) and the audio and image features are effectively fused through a late fusion strategy. The proposed algorithm shows excellent performance on multiple independent test sets, and the evaluation indicators such as classification accuracy, precision, recall rate, and F1 score all reach a high level, demonstrating the model's generalization ability for different music types.