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Train RNN to generate piano music

Music Generation Deep Learning RNN
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This workflow uses preprocessed midi files to train a many to many RNN to generate music. The brown nodes in the upper part define the network architecture. The chosen network architecture has 5 inputs for - the notes - the duration - the offset difference to the previous note - the initial hidden states of the LSTM After an LSTM layer the network splitt into three, parallel, feedforward subnetworks with different activation functions: - one for the notes - one for the duration - one for the offset difference Afterwards the three subnetworks are collected. In the Keras Network Learner node the Loss function is defined by selecting a loss for each feedforward subnetwork. - Categorical Cross Entropy for the notes - MSE for the duration and th offset difference.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.1
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

    knime
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    KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    knime
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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

    knime
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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

    knime
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