用分数傅里叶变换+LSTM生成接近真人水平的音乐
A novel LSTM music generator based on the fractional time-frequency feature extraction
- 结合分数傅里叶变换提取音乐时频特征，再用LSTM建模生成
- 在GiantMIDI-Piano数据集上生成音乐质量接近人类创作
- 适合对音乐生成、时频分析感兴趣的开发者和研究者
本文提出一种基于人工智能的新型音乐生成方法。通过分析音乐特征并进行拟合与预测来生成新音乐。该方法以分数傅里叶变换（FrFT）和长短期记忆网络（LSTM）为基础：FrFT用于提取音乐信号在时间-频率域上的谱特征；LSTM则基于提取特征，结合隐藏层状态与实时输入生成新音乐。实验基于GiantMIDI-Piano数据集进行，结果表明所提系统能生成与人类创作水平相当的高质量音乐。
原文摘要 · Abstract (English)
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.
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