| dc.contributor.author | CHOI, K | en_US |
| dc.contributor.author | sandler, M | en_US |
| dc.contributor.author | fazekas, G | en_US |
| dc.contributor.author | Conference on Computer Simulation of Musical Creativity | en_US |
| dc.date.accessioned | 2016-05-26T13:05:46Z | |
| dc.date.available | 2016-04-15 | en_US |
| dc.date.issued | 2016-06-18 | en_US |
| dc.date.submitted | 2016-04-27T17:15:18.771Z | |
| dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/12552 | |
| dc.description.abstract | In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progressions and drum tracks in two case studies. In the experiments, word-RNNs (Recurrent Neural Networks) show good results for both cases, while character-based RNNs (char-RNNs) only succeed to learn chord progressions. The proposed system can be used for fully automatic composition or as semi-automatic systems that help humans to compose music by controlling a diversity parameter of the model. | en_US |
| dc.rights | arXiv record http://arxiv.org/abs/1604.05358. Presented at Conference on Computer Simulation of Musical Creativity | |
| dc.subject | automatic composition | en_US |
| dc.subject | lstm | en_US |
| dc.subject | rnn | en_US |
| dc.subject | algorithmic composition | en_US |
| dc.title | Text-based LSTM networks for Automatic Music Composition | en_US |
| dc.type | Conference Proceeding | |
| pubs.notes | No embargo | en_US |
| pubs.organisational-group | /Queen Mary University of London | |
| pubs.organisational-group | /Queen Mary University of London/Faculty of Science & Engineering | |
| pubs.organisational-group | /Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Electronic Engineering - Research Students | |
| pubs.organisational-group | /Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Staff | |
| pubs.organisational-group | /Queen Mary University of London/Faculty Reporting - Research Students | |
| pubs.organisational-group | /Queen Mary University of London/Faculty Reporting - Research Students/Faculty of Science & Engineering PGRs | |
| pubs.organisational-group | /Queen Mary University of London/REF | |
| pubs.organisational-group | /Queen Mary University of London/REF/REF - UoA 11 | |
| pubs.publication-status | Accepted | en_US |
| dcterms.dateAccepted | 2016-04-15 | en_US |
| qmul.funder | Fusing Semantic and Audio Technologies for Intelligent Music Production and Consumption::Engineering and Physical Sciences Research Council | en_US |
