Instrument Emotion Recognition from Polyphonic Instrumental Music using MFCC and CENS Features with Deep Neural Networks
Sangeetha Rajesh, N. J. Nalini · Procedia Computer Science · 2024
The music digital data size is escalating in a manner where efficiently organizing and retrieving the data based on various parameters become tedious. Instrument music emotion recognition (IER) can be effectively utilized to recognize the emotion from instrumental music using machine learning methods. It has wide range of applications in music industry, psychology, medicine, and entertainment. Extracting or selecting suitable features that impact emotion recognition is one of the main tasks in IER. In this work, Mel Frequency Cepstral Coefficients (MFCC) and Chroma Energy Normalized Statistics (CENS) features have been employed to provide information to recognize the emotion pattern in instrumental music. These features have been trained using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) which captures the high dimensional emotion patterns. A recognition rate of 89.7% and an equal error rate of 10.3% have been attained using CNN by combining the features. The experiment results depict that the recognition rate of each instrument-emotion class and the overall performance is improved by the score level fusion of the MFCC and CENS features compared to individual features.