Papers › Everybody Compose: Deep Beats To Music

Everybody Compose: Deep Beats To Music

9 Jun 2023arXiv:2306.06284archive 2025-07-28

Conghao Shen, Violet Z. Yao, Yixin Liu

This project presents a deep learning approach to generate monophonic melodies based on input beats, allowing even amateurs to create their own music compositions. Three effective methods - LSTM with Full Attention, LSTM with Local Attention, and Transformer with Relative Position Representation - are proposed for this novel task, providing great variation, harmony, and structure in the generated music. This project allows anyone to compose their own music by tapping their keyboards or ``recoloring'' beat sequences from existing works.

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout LSTM Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Sigmoid Activation Softmax Tanh Activation Transformer

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