Research on the Construction of Music Recognition Model Based on Improved CNN Convolutional Neural Network

In the context of today's informationized society, music recognition technology within the field of intelligent audio processing has received extensive attention and become a research focus. However, traditional music recognition techniques are difficult to adapt to the complex changes of music signals due to the inherent defects in feature extraction and model construction. To address these difficulties, this study constructs an innovative music recognition algorithm, which is based on an optimized convolutional neural network (CNN). The efficacy of feature extraction is enhanced by fusing the hash convolutional neural network (Hash-CNN), and the temporal data is processed using the long short-term memory network (LSTM) to improve the accuracy of recognition. In the preprocessing stage of music signals, we applied various noise reduction and normalization means to enhance the data quality to a high standard. According to the experimental data, the model performs well in recognizing complex music segments, especially when analyzing multi-level and multi-dimensional music features, it shows strong robustness. Meanwhile, compared with traditional techniques, the model proposed in this study shows significant improvement in both recognition efficiency and accuracy, confirming its great potential and broad prospect in practical applications.