[1]MIAO Beichen,GUO Weian,WANG Lei.A polyphony music generation system based on latent features and a recurrent neural network[J].CAAI Transactions on Intelligent Systems,2019,14(1):158-164.[doi:10.11992/tis.201804009]
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A polyphony music generation system based on latent features and a recurrent neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 1 Page number: 158-164 Column: 学术论文—机器感知与模式识别 Public date: 2019-01-05
Title:
A polyphony music generation system based on latent features and a recurrent neural network
Author(s):
MIAO Beichen1;  GUO Weian2;  WANG Lei1
1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
2. College of China and German, Tongji University, Shanghai 201804, China
Keywords:
music generation; latent feature extraction; recurrent neural network; stacked autoencoder; polyphony music; sequence prediction; long short-term memory; generation model
CLC:
TP393.04
DOI:
10.11992/tis.201804009
Abstract:
Music generation is a research area that uses algorithms to generate sequences with characteristics of music. Focusing on the problem of feature extraction from music samples and automatic music compositions, this paper proposes a polyphony music generation algorithm based on musical latent features and a recurrent neural network (RNN). The proposed algorithm uses a stacked autoencoder to extract latent features from of music sequence notes at each time step; the algorithm then uses long-short term memory RNNs to build a music generation system in the form of sequence prediction. The simulation results show that this algorithm can generate polyphony music with better melody and chord matching.
References:
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