DEEP COMPOSER: DEEP NEURAL HASHING AND RETRIEVAL APPROACH TO AUTOMATIC MUSIC GENERATION

被引:5
|
作者
Royal, Brandon [1 ]
Hua, Kien [1 ]
Zhang, Brenton [1 ]
机构
[1] Univ Cent Florida, Orlando, FL 32816 USA
来源
2020 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) | 2020年
关键词
Music Generation; Music Retrieval; Hashing; Deep Learning;
D O I
10.1109/icme46284.2020.9102815
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
With recent advances in artificial intelligence, recurrent neural networks have successfully generated pleasing melodies; however, they have struggled to create a full song that has structure, theme, and uniqueness. To overcome this limitation, we introduce Deep Composer, a music generation system based on music retrieval using deep neural hashing to encode the music building blocks. Music generation is performed by using the current music segment to retrieve the next segment from the database until the whole composition is complete. We present performance comparisons with multiple recent state-of-the-art music generation methods to show that the songs generated by Deep Composer are unique, musically pleasing and contain more structure and theme features like that of a professionally composed song.
引用
收藏
页数:6
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