A Multi-sentence Music Humming Retrieval Algorithm Based on Relative Features and Deep Learning

Abstract

This project will study a fast retrieval method for music humming speech recognition based on sentence features and deep learning. The method proposed in this paper can realize the fast extraction of songs. According to the characteristics of the natural pause mode of the song, the song database and the song fragments provided by the user are divided into different sentences. The deep learning algorithm of BDTW is used to calculate the similarity of the song's pitch, and users can set matching conditions according to their preferences. It can identify the most significant differences between music fragments and the order of queries in the database. Then, a retrieval method of a music database based on DIS is proposed. It can shorten the acquisition time. Experiments show that the algorithm can recognize humming songs quickly and efficiently.