Brain Cognition of Musical Features Based on Automatic Acoustic Event Detection

Music is an essential carrier of information. It is an ideal start to reveal the function of the human brain. But few researches focus on the cognition of musical features in the real listening state. Moreover, it is hard to compare their acoustic characteristics, because the experimental paradigms are different. The purpose of this research is to investigate the cognitive analysis method of musical features in the natural listening state. Firstly, the sliding window technology was applied to the musical features to detect the event points automatically. Then, the auditory event-related potentials (AERP) were calculated based on these event points. In addition to the N1-P2 component, the acoustic late positive potential (LPP) has also been found, which further proved the effectiveness of this method. Finally, the cognitive process of acoustic features was revealed by topographic maps, which was a solid foundation for further neural pathways analysis. The method proposed in this paper provides a new idea for the study of musical features. Through the in-depth analysis, it can uncover the cognitive mechanism of the brain for acoustic features, and provide a solid foundation for music computing and composition.