A novel LSTM music generator based on the fractional time-frequency feature extraction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908978631409664 |
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| author | Ya, Li Wei, Chen Xiulai, Li Lei, Yu Xinyi, Deng Chaofan, Chen |
| author_facet | Ya, Li Wei, Chen Xiulai, Li Lei, Yu Xinyi, Deng Chaofan, Chen |
| contents | In this paper, we propose a novel approach for generating music based on an artificial intelligence (AI) system. We analyze the features of music and use them to fit and predict the music. The fractional Fourier transform (FrFT) and the long short-term memory (LSTM) network are the foundations of our method. The FrFT method is used to extract the spectral features of a music piece, where the music signal is expressed on the time and frequency domains. The LSTM network is used to generate new music based on the extracted features, where we predict the music according to the hidden layer features and real-time inputs using GiantMIDI-Piano dataset. The results of our experiments show that our proposed system is capable of generating high-quality music that is comparable to human-generated music. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17823 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A novel LSTM music generator based on the fractional time-frequency feature extraction Ya, Li Wei, Chen Xiulai, Li Lei, Yu Xinyi, Deng Chaofan, Chen Sound Artificial Intelligence Computation and Language In this paper, we propose a novel approach for generating music based on an artificial intelligence (AI) system. We analyze the features of music and use them to fit and predict the music. The fractional Fourier transform (FrFT) and the long short-term memory (LSTM) network are the foundations of our method. The FrFT method is used to extract the spectral features of a music piece, where the music signal is expressed on the time and frequency domains. The LSTM network is used to generate new music based on the extracted features, where we predict the music according to the hidden layer features and real-time inputs using GiantMIDI-Piano dataset. The results of our experiments show that our proposed system is capable of generating high-quality music that is comparable to human-generated music. |
| title | A novel LSTM music generator based on the fractional time-frequency feature extraction |
| topic | Sound Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.17823 |