Automatic Music Generation using Long Short-Term Memory Neural Networks
Mannmay Mukesh Vinze, Jeevan Danve, Srijan Shankar, Meraz Khan, Nilima Kulkarni
Recent developments in neural networks and sequential models has produced state of the art results in eld signal processing and sequential data generation. For us, music is a pleasing sound and everyone listens to music very frequently but computers represent it as an sequential data and all sequential data generation model can be used to generate music. Our work focuses on generating music using LSTMs. LSTMs have good capability to remember previous data, they have memory unlike general RNNs. A good music must not be abruptly changing tones and themes, it must be consistent and for this purpose our model must remember what was generated previously. So, LSTMs are the perfect choice for this context based data generation. We used Keras[2], an open-source software library that provides a Python interface for arti cial neural networks, to create and train model. Most impressive results were produced by Multi-layered Char-RNN with LSTM Cell. The data is represented with ABC le format for easier access and better understanding. We preprocess the data to make it more robust and understandable for neural network and decode it back for human interpretation, the preprocessing algorithms and data representation is thoroughly discussed. The model used in this paper learn the sequences of polyphonic musical notes over a Stacked-Multilayered Char-RNN with LSTM cell. The model required and do have have the potential to recall past details of a musical sequence and its structure for better learning because of memory cells present in LSTM cells. The whole architecture with data ow and training and testing scores are explained.
Article Details
Unique Paper ID: 150859

Publication Volume & Issue: Volume 7, Issue 10

Page(s): 176 - 180
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