Machine Learning
Music Generation with Deep Learning
LSTM-based sequence model for generated music notes
Overview
A PyTorch deep learning project that encodes note sequences and trains an LSTM model for music generation.
Problem
Music generation requires modeling temporal structure while keeping training stable across long note sequences.
Dataset
Encoded music-note sequences represented as 129-dimensional one-hot vectors prepared with Pandas.
Approach
Implemented an LSTM model in PyTorch, improved training stability with Batch Normalization and Dropout, and evaluated sequence-level performance.
Results
Achieved a 3.7x performance gain and significantly reduced test loss compared with the baseline model.
Lessons Learned
Sequence generation benefits from careful representation design and regularization, not only a larger neural architecture.
Model / Pipeline
The implementation combines Python, PyTorch, Pandas, LSTM, Deep Learning with a repeatable workflow for data preparation, evaluation, and communication.