Piano Composer.

Piano componser music sensations.

This model allows you to train a neural network to generate midi music files that make use of a single instrument focused on different rhythms and genres, with modulation of sensations and emotions transmitted through music.

The model utilizes a Long Short-Term Memory (LSTM) neural network architecture. LSTM is a type of recurrent neural network (RNN) that is well-suited for modeling sequential data, such as music, due to its ability to capture long-term dependencies. Input Layer: The input to the model is a sequence of musical notes, represented as MIDI data. The music21 library is used to load and process the MIDI files. LSTM Layers: The core of the model is composed of one or more LSTM layers. LSTMs are capable of learning complex patterns in the input sequences and generating new sequences that exhibit similar characteristics. Dense (Fully Connected) Layers: After the LSTM layers, the model includes one or more dense layers. These layers allow the model to learn higher-level representations and patterns in the generated music. Dropout Layers: Dropout layers are included to prevent overfitting and improve the model's generalization capabilities. Batch Normalization Layers: Batch normalization layers are used to stabilize the training process and improve the model's performance. Output Layer: The final layer of the model is an output layer that generates the next note in the sequence, based on the input and the learned patterns. The output is a probability distribution over the possible notes, which can be sampled to generate new music. Training and Generation Data Preprocessing: The MIDI files are loaded and preprocessed using the music21 library. This includes converting the MIDI data into a format suitable for the model's input. Model Training: The LSTM-based model is trained on the preprocessed MIDI data using the Keras deep learning library. The model is trained to predict the next note in the sequence, given the previous notes. Model Checkpointing: During training, the model's weights are periodically saved using the ModelCheckpoint callback in Keras. This allows the model to be resumed from the last saved checkpoint, in case the training needs to be interrupted. Music Generation: Once the model is trained, it can be used to generate new MIDI music files. The model is initialized with a seed sequence of notes, and it then generates new notes by iteratively sampling from the output probability distribution. Emotion and Sensation Modulation: The model can be further fine-tuned or conditioned on specific emotional or sensational attributes, allowing the generated music to evoke desired moods or sensations. This can be achieved by incorporating additional input features or by using techniques like conditional generation.