Overview / Usage

The music generator application with LSTM and RNN neural network is a project that uses machine learning techniques to generate music. The application is designed to take in a dataset of existing music, and then use an LSTM (Long Short-Term Memory) or RNN (Recurrent Neural Network) model to learn the patterns and structure of the music.

Methodology / Approach

LSTM (Long Short-Term Memory) networks are a type of recurrent neural network (RNN) that are capable of modeling sequential data. They have been used successfully in various applications, including natural language processing, speech recognition, and music generation.

To generate music using an LSTM network, the input to the network is typically a sequence of musical events, such as notes, chords, and rhythms. The network is trained on a large corpus of existing music, learning patterns and relationships between these musical events.

During the training process, the LSTM network learns to predict the next musical event in the sequence based on the input sequence and the current state of the network. The output of the network is a probability distribution over all possible musical events at each time step.

To generate music, the LSTM network is seeded with an initial sequence of musical events, and then it generates new musical events one at a time based on the probabilities predicted by the network. The process of generating new music is typically iterative, with each new event being fed back into the network as input for the next prediction.

By adjusting the hyperparameters of the LSTM network, such as the number of layers, the number of hidden units, and the learning rate, it is possible to control the style and complexity of the generated music. Additionally, by training the LSTM network on different styles of music, it is possible to generate music in a variety of genres and styles.

Repository

https://github.com/rookierohan10/MusicProject

Collaborators