With the rapid development of computer technology, the dynamic visualization of vocal music melody has attracted more and more attention. The dynamic visualization of vocal music melody is a way of expression and presentation of music. The dynamic visualization of vocal music melody is a way to express more music information in various forms with vision as the center and with music, which is a new form of cultural exchange in the information age. This paper focused on the dynamic visualization of vocal melody, and proposed audio feature extraction based on auto correlation function (ACF) and improved ACF algorithm. The improved ACF algorithm was more efficient in extracting audio features, which was also conducive to the dynamic visualization of vocal music melody. The experimental results in this paper showed that the extraction time of ACF algorithm and improved ACF algorithm on MIR-1K dataset was 10.8s and 3.6s respectively when the pitch was 180, and the extraction time of ACF algorithm and improved ACF algorithm on MedleyDB dataset was 12.3s and 2.8s respectively when the pitch was 180. It can be found that the extraction time of the improved ACF algorithm was less than that of the ACF algorithm, which also showed that the improved ACF algorithm had higher extraction efficiency.
