A novel LSTM music generator based on the fractional time-frequency feature extraction
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 20 Apr 2026.
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References · 8
- 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
- 2020Attention is All You Need in Speech Separationcited by 185
- 2017LSTM Fully Convolutional Networks for Time Series Classificationcited by 136
- 2017Music Generation by Deep Learning - Challenges and Directionscited by 24
- 2020RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learningcited by 19
- 2020From Artificial Neural Networks to Deep Learning for Music Generation -- History, Concepts and Trendscited by 14
- 2022An adaptive music generation architecture for games based on the deep learning Transformer modecited by 3
- 2016Composing Music with Grammar Argumented Neural Networks and Note-Level Encodingcited by 1
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