Abstract: Multiple fundamental frequency estimation is widely used in music structure analysis,music aided education,information retrieval and other fields.In order to meet the requirements of accurate identification of random chords in music,a multiple fundamental frequency estimation algorithm based on generative adversarial networks is proposed.Firstly,the complete audio is divided into note segments,and a homophonic fingerprint is proposed to extract the spectrum characteristics of the note segment.Then,the current dominant fundamental frequency of the homophonic fingerprint is identified by convolution neural network,and the identified dominant fundamental frequency is considered as the image that interferes with the next fundamental frequency re-cognition.Then,the interference image is removed by generative adversarial networks,and the homophonic fingerprint image affected by interference is processed in a new round.Finally,the multiple fundamental frequency estimation of complete chords is realized by iterative de imaging operation step by step.Experiments on the piano audio database composed of random two tone chord and random three tone chord are carried out.The results show that,compared with the classical spectrum iterative deletion algorithm and the large vocabulary chord recognition algorithm,the algorithm in this paper can adapt to the recognition of random chords,has high robustness in different ranges,and improves the overall accuracy significantly.

Key words: Convolution neural network, Fundamental frequency image, Generative adversarial networks, Homophonic fingerprint, Multiple fundamental frequency estimation