Individualized song recommending system based on vocal music characteristics

Abstract

The embodiment of the invention discloses an individualized song recommending system based on vocal music characteristics. The method includes the following steps of extracting characteristics, wherein the voice register characteristics, speed characteristics and tone characteristics of singing data are extracted, the voice register characteristics include absolute voice register and relative voice register, the speed characteristics include the number of beats per minute, and the tone characteristics include a Gaussian mixture model of Mel frequency cepstrum coefficient training; recommending songs systematically, wherein a corresponding song in a music library is found through a key sound matching algorithm according to a snatch sung by a user, and voice register conformity detection, song conformity detection and singer conformity detection are conducted. Singer recommending and song recommending are conducted according to extracted user characteristics. By means of the method, whether a current song is suitable for being sung by the user or not can be evaluated, and singers matched with the user vocal music ability and songs suitable for being sung by the user are further recommended. From the perspective of user singing, the traditional music recommending range is expanded, and higher practical value is achieved.

Description

technical field [0001] The invention belongs to the field of audio processing, and emphatically describes a personalized song recommendation system based on singing content. Background technique [0002] Music recommendation is a technology that provides users with music that matches their personal interests. This technology has important research and application value in today's rapid development of digital music and rapid growth of music resources. Song recommendation is a branch of music recommendation. With the vigorous development of the song market today, song recommendation has become the key development direction of the song market. [0003] At present, the main methods of music recommendation include content-based recommendation, collaborative filtering recommendation, association rule-based recommendation, knowledge-based recommendation and combination recommendation, etc. Content-based recommendation is mainly aimed at the acoustic properties of the music itself...

AI Technical Summary

Problems solved by technology

The music obtained by this recommendation method is often highly rated and popular music, and some newly added music is difficult to be recommended to users because they do not have enough time to pay attention

In traditional content-based recommendations, manual annotation is generally used when extracting abstract parameters such as emotion. In this way, the expression of features is mixed with subjective components and has some uncertain factors.

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