Feature Extraction and Classification of Music Content Based on Deep Learning

Abstract

To study the use of in-depth training in extracting and classifying the content of music samples, the work offers an algorithm for identifying and classifying musical genres based on a deep network of beliefs, enabling it to be used to extract and classify traditional Chinese musical instruments, and using real-world experiments to test its performance after training. The experimental results are as follows: the improved depth confidence network algorithm has the highest accuracy for music recognition and classification, which can reach 75.8%, higher than other traditional methods. The improved depth confidence network identifies and classifies Chinese traditional musical instruments through Softmax layer, and the accuracy is even as high as 99.2%; DBN is combined with Softmax neural network algorithm when only a few labeled samples in the training set are used for network fine-tuning, and the accuracy of the algorithm can still reach more than 90%, which can reduce the workload in the early stage. This study effectively solves the problem of too much workload and low accuracy in the process of music content recognition, classification, and extraction.

Music Tone Synthesis based Anti-Interference Dynamic Integral Neural Network optimized with Artificial Hummingbird Optimization Algorithm

“…Shi and Co [24] have presented feature extraction and classification of music content depend on deep learning. The presented paper researches the advantage of comprehensive instruction in identifying and classifying the satisfied with music sampling, the use of an algorithm to recognize and order musical genres based on a deep philosophical network, and the ability to remove and classify conventional musical instruments, through real-world research to trial its show after training.…”