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Author:

Hung, Ying Kai (Hung, Ying Kai.) | Pei, Yan (Pei, Yan.) | Li, Jianqiang (Li, Jianqiang.)

Indexed by:

EI Scopus

Abstract:

This research presents a new music generation model and a novel MIDI data format for MIDI music generation. This innovative data format allows us to process MIDI music in a manner analogous to video analysis. Initially, the model employs Convolutional Neural Networks (CNN) as an encoder to effectively capture local and global features within the musical data. Subsequently, we utilize a Transformer as a decoder, leveraging its self-attention mechanism to handle the long-term dependencies present in music data. In the training process, an interactive chaotic algorithm is introduced to update the model’s weights, assisting the model in avoiding entrapment in local optima. This enhances the learning efficiency of the model and improves the quality of the generated output, enabling the model to generate music, including accompaniment, that aligns with human aesthetics from any given melody. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

Keyword:

Music Convolutional neural networks Convolution

Author Community:

  • [ 1 ] [Hung, Ying Kai]Graduate School of Computer Science and Engineering, University of Aizu, Fukushima, Aizuwakamatsu; 965-8580, Japan
  • [ 2 ] [Pei, Yan]Graduate School of Computer Science and Engineering, University of Aizu, Fukushima, Aizuwakamatsu; 965-8580, Japan
  • [ 3 ] [Li, Jianqiang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

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ISSN: 1876-1100

Year: 2024

Volume: 1134

Page: 211-221

Language: English

Cited Count:

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SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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