LSTM

The sound that is generated with the help of musical instruments or human voice is called a music note. It is a simple unit of music.

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To develop a model and to understand the model’s complexity it is necessary that the model is to be trained depending upon the nature of the input. The model which is supposed to be trained needs to recall the former details and needs to create a rational piece.

The method of generating music is to utilize the existing music which is called a genetic algorithm.

It can highlight the strong rhythm in each fragment and can also join them into distinct pieces of music. It delays the iteration process because it has low efficiency.

It also creates the problem to get coherence and rhythm which is deep-seated and also lacks in the context.

Hence to overcome the above problem and to remember the previous note sequence and predict the next sequence Long Short-term Memory (LSTM) is used

Project Implementation

To design a deep neural network

• The layer of LSTM depends on a selected input
• All the notes are not trained in LSTM but only a few are trained, which tunes the model effectively
• The model with the inputs learns to map and correlate between notes and their projection
• Now the dropout layer is used to create generalizations in the model

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• All the LSTM cells are combined when the LSTM learns the probability distributions of notes and its sequences
• The gap in the dense layer is now subordinated. This dense layer ensures that the model is fully connected
• At last, an endpoint is added to the activation layer which helps to decide, which neurons should be activated. This also helps to decide whether the information gained by the neuron is relevant which makes the activation function highly important in a deep neural network.
• Now the model generates a new sequence of musical notes which helps to ensure the better prediction

Conclusion

From the above project, the following can be concluded

1. It was found that the model was successfully designed and generated music without any human interference
2. The model was successfully able to recall the details of dataset and generated polyphonic music using a single-layered LSTM model
3. The model was so self-learning that it learned harmonic and melodic note from MIDI files of pop music

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LSTM