Towards Music Generation With Deep Learning Algorithms
Date Issued
2018
Author(s)
Docevski, Marko
Abstract
Computer music generation has application in many
areas, including computer aided music composition, on demand
music generation for video games, sport events, multi-media
experiences, creating music in the style of passed away artists, etc.
In this work we describe our approach towards music generation.
We trained a deep learning model on a corpus of works of several
authors. By priming the model with a snippet of an authors
work we used it to create new music in their style. The dataset
consists of music for guitar in midi format, containing only 1
part/instrument. We gathered more than 2000 files, of which we
used from 5 to 300 per experiment. The data for the deep learning
model is represented in piano roll format, a binary matrix where
one axis represents the time and the other axis represents midi
notes. Two deep learning architectures were evaluated, a 2-layer
recurrent neural network of LSTM (Long Short Term Memory)
cells and an Encoder-Decoder (Auto-Encoder) architecture for
sequence learning, where both the encoder and decoder are built
as recurrent layers of LSTM cells. The models were implemented
in the Keras deep-learning library. The results were evaluated
on a subjective basis, and with the evaluated datasets both
architectures produced results of limited quality.
areas, including computer aided music composition, on demand
music generation for video games, sport events, multi-media
experiences, creating music in the style of passed away artists, etc.
In this work we describe our approach towards music generation.
We trained a deep learning model on a corpus of works of several
authors. By priming the model with a snippet of an authors
work we used it to create new music in their style. The dataset
consists of music for guitar in midi format, containing only 1
part/instrument. We gathered more than 2000 files, of which we
used from 5 to 300 per experiment. The data for the deep learning
model is represented in piano roll format, a binary matrix where
one axis represents the time and the other axis represents midi
notes. Two deep learning architectures were evaluated, a 2-layer
recurrent neural network of LSTM (Long Short Term Memory)
cells and an Encoder-Decoder (Auto-Encoder) architecture for
sequence learning, where both the encoder and decoder are built
as recurrent layers of LSTM cells. The models were implemented
in the Keras deep-learning library. The results were evaluated
on a subjective basis, and with the evaluated datasets both
architectures produced results of limited quality.
Subjects
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