[1]CHEN Zhuanghao,ZHANG Maoqing,GUO Weian,et al.Music lyrics-melody intelligent evaluation algorithm based on sequence model[J].CAAI Transactions on Intelligent Systems,2020,15(1):67-73.[doi:10.11992/tis.202001006]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 1
Page number:
67-73
Column:
学术论文—机器感知与模式识别
Public date:
2020-01-05
- Title:
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Music lyrics-melody intelligent evaluation algorithm based on sequence model
- Author(s):
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CHEN Zhuanghao1; ZHANG Maoqing1; GUO Weian2; KANG Qi1; WANG Lei1
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1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
2. Sino-German College of Applied Sciences, Tongji University, Shanghai 201804, China
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- Keywords:
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music lyrics-melody; emotion; rhythm; sequence model; lyrics encoder; melody encoder; matching decoder; lyrics-melody matching degree; music lyrics-melody matching
- CLC:
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TP393.04
- DOI:
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10.11992/tis.202001006
- Abstract:
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Emotional matching model is a method often used to evaluate the degree of lyrics and melody matching. However, it cannot be accurately evaluated based on the emotion matching model. In order to improve it, this paper proposes an intelligent evaluation algorithm of lyrics-melody matching based on a sequence model, which comprehensively considers the emotion and the rhythm relationship between lyrics and melody to give an evaluation method for more accurate evaluation. Firstly, this paper researches and builds music positive and negative samples considering music emotion and phrase based on the public lyrics-melody paired dataset and divide songs to music pieces. Further, the lyrics and melody fragments are encoded by the lyrics-encoder and the melody-encoder, respectively. And take the encoded lyrics feature and melody feature that are contextualized as the input of the lyrics-melody matching decoder to analyze the characteristic relationship between the lyrics and melody, and then determine the matching degree of the lyrics-melody segment. The experimental results show that the music lyrics-melody matching intelligent evaluation algorithm model based on sequence model can more accurately judge the matching degree of lyrics-melody matching than simple music emotion matching, which verifies the effectiveness of the proposed algorithm.