A study on the classification of Chinese folk instruments based on deep learning

Abstract

Due to the Internet's tremendous expansion in recent years, digital music has seen explosive growth. It is becoming increasingly important to provide fast and accurate music retrieval for users, and music information retrieval has gradually become a research hotspot. Among them, the classification of musical instruments is one of the hot research directions. As an important part of the world's musical instruments, the Chinese folk musical instrument tradition has great research value, but there is less research in this direction. Traditional music classification methods can be roughly divided into two steps: manual feature extraction and traditional machine learning. But traditional music classification has several shortcomings, as follows. Firstly, manual extraction of music features has a high error rate and it is very difficult to ensure the accuracy of the features, and the labor cost is high; secondly, traditional machine learning still has problems with multiple classifications and training on large scale data. Therefore, in this paper, based on previous research, the original data features are extracted from the Mel Frequency Cepstrum Coefficient (MFCC), which is a feature that can represent musical timbre, and deep learning is proposed to be used to classify traditional Chinese folk instruments. This paper finds that the results of identifying and classifying Chinese folk musical instruments using different classical classification methods are compared when the training set and test set data are the same. According to the experimental results, the use of KNN has the best performance among these classification methods.