The Classification and Judgment of Abnormal Problems in Music Song Interpretation Based on Deep Learning

Abstract

In this paper, we propose a deep learning-based method for classifying abnormal judgments of music song interpretation problems, using a computer to record the singer’s voice and then analyze and judge it with a trained method model, pointing out the main problems that exist in the song singing process, which is of practical significance for the singer’s self-learning and related teaching guidance. We collected more than 300 singers’ audio, and the data was calibrated and classified by professional researchers in the field of music, mainly divided into seven categories: vocal pallor, inability to keep up with the rhythm, running out of tune, lack of breath, unclear spitting, narrow range (the highs can’t go up and the lows can’t come down), and true and false voice problems. The audio feature extraction process applies Short Time Fourier Transform (STFT), Mel Frequency Cepstrum Coefficient (MFCC), and spectral prime to extract the features of song audio, and a randomly selected part of the audio is used as the training and validation dataset. This dataset is trained using a residual neural network and efficient neural network. The experimental results of our model show that the data training accuracy is about 90.1%, which achieves good results.