Multiple Musical Instrument Signal Recognition Based on Convolutional Neural Network

Abstract

To improve the accuracy of multi-instrument recognition, based on the basic principles and structure of CNN, a multipitch instrument recognition method based on the convolutional neural network (CNN) is proposed. First of all, the pitch feature detection technology and constant Q transform (CQT) are adopted to extract the signal characteristics of multiple instruments, which are used as the input of the CNN network. Moreover, in order to improve the accuracy of multi-instrument signal recognition, the benchmark recognition model and two-level recognition model are constructed. Finally, the above models are verified by experiments. The results show that the two-level classification model established in this article can accurately identify and classify various musical instruments, and the recognition accuracy is improved most obviously in xylophone. Compared with the benchmark model, the constructed two-level recognition has the highest accuracy and precision, which shows that this model has superior performance and can improve the accuracy of multi-instrument recognition.