Everybody Compose: Deep Beats To Music
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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Colab
Tasks
Datasets
MAESTRO
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Methods
Absolute Position Encodings •
Adam •
BPE •
Dense Connections •
Dropout •
Label Smoothing •
Layer Normalization •
Linear Layer •
LSTM •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Sigmoid Activation •
Softmax •
Tanh Activation •
Transformer
