Automatic music transcription based on convolutional neural network, constant Q transform and MFCC

Abstract

Automatic music transcription is a function that relies on the efficiency of the computer's own calculation speed to recognize the pitch of external input audio and output it accurately. In the past methods, there are often problems such as inability to reflect music continuity or narrow identifiable frequency range. In order to avoid such problems, two methods different from the previous Mel Cepstral Coefficients and Constant Q Transform are selected to extract the features of music, and convolutional neural networks are used for training and recognition. Among them, the Mel Cepstrum coefficient is used to judge the timbre, and the constant Q transform is used to judge the pitch. After inputting the corresponding features into the neural network for training and learning, we can find that the recognition success rate has reached 95%.