Song indexing method and device, equipment, medium and product

Abstract

The invention discloses a song indexing method and device, equipment, a medium and a product, and the method comprises the steps: encoding audio information in song audio data, and obtaining corresponding encoding information; inputting the coding information into a common network of a feature extraction model trained to a convergence state, and sequentially performing multi-stage feature extraction on the coding information through a plurality of convolution blocks in the common network to obtain intermediate feature information; the intermediate feature information is input into more than two branch networks of the feature extraction model, feature extraction is carried out on the intermediate feature information through a plurality of convolution blocks in each branch network, then the intermediate feature information is converted into corresponding output feature vectors, and deep semantic information contained in the output feature vectors of the branch networks is different; and taking the output feature vector as a high-dimensional index vector for representing deep semantic information of the song audio data. According to the method, the representation learning ability of the deep semantic information of the song audio data is improved.

Description

technical field [0001] The present application relates to the technical field of music information retrieval, and in particular, to a song indexing method and its corresponding apparatus, computer equipment, computer-readable storage medium, and computer program product. Background technique [0002] With the popularity of short videos, live broadcasts, and radio stations, the amount of cover music is increasing, and the scenes that require music recognition are becoming more and more complex. Compared with the original version, the cover version may have differences or even completely different musical components such as timbre, fundamental frequency, rhythm, speed, harmony, lyrics, singing style, and overall structure. Therefore, cover recognition is a very challenging research work. [0003] There are a variety of technologies related to cover recognition in the prior art, and various prior technologies all have a certain degree of deficiencies, such as: (1) traditional ...

AI Technical Summary

Problems solved by technology

[0003] There are many technologies related to cover recognition in the prior art, and all existing technologies have certain deficiencies, for example: (1) the traditional technology of listening to songs and recognizing songs based on Landmark can only recognize songs of the same source, Unable to recognize the above-mentioned cover version with certain differentiated information; (2) The traditional humming recognition technology based on melody matching can only recognize clean a cappella / humming, but cannot recognize the above-mentioned cover version with background accompaniment; (3) The traditional cover recognition technology scheme mainly extracts audio features such as Pitch Class Profile (PCP), and then uses algorithms such as dynamic programming to calculate the similarity distance between songs

Due to the diversity of cover versions, the above scheme is only suitable for adapting smaller cover solutions, the accurate recognition rate is low, and the recognition speed is slow, so it cannot be applied to the search of massive music

[0004] Therefore, the technical solutions related to song recognition in the prior art lack universal adaptability, the recognition accuracy is not high, and the recognition efficiency is low. It is necessary to explore more effective technical solutions

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