Subject: music generation, midi, deep learning, recurrent neural networks, LSTM, auto-encoder
Year: 2018
Type: Proceedings
Title: Towards Music Generation With Deep Learning Algorithms
Author: Docevski, Marko
Author: Zdravevski, Eftim
Author: Lameski, Petre
Author: Kulakov, Andrea
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.
Publisher:
Relation: CIIT 2018
Identifier: oai:repository.ukim.mk:20.500.12188/22264
Identifier: http://hdl.handle.net/20.500.12188/22264
