Learning audio features for genre classification with deep belief networks
| dc.contributor.author | Noolu, Satya | |
| dc.contributor.supervisor | Tzanetakis, George | |
| dc.date.accessioned | 2018-12-04T19:52:35Z | |
| dc.date.available | 2018-12-04T19:52:35Z | |
| dc.date.copyright | 2018 | en_US |
| dc.date.issued | 2018-12-04 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Feature extraction is a crucial part of many music information retrieval(MIR) tasks. In this project, I present an end to end deep neural network that extract the features for a given audio sample, performs genre classification. The feature extraction is based on Discrete Fourier Transforms (DFTs) of the audio. The extracted features are used to train over a Deep Belief Network (DBN). A DBN is built out of multiple layers of Randomized Boltzmann Machines (RBMs), which makes the system a fully connected neural network. The same network is used for testing with a softmax layer at the end which serves as the classifier. This entire task of genre classification has been done with the Tzanetakis dataset and yielded a test accuracy of 74.6%. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/10380 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Neural Networks | en_US |
| dc.subject | Deep Belief Networks | en_US |
| dc.subject | Music Information Retrieval | en_US |
| dc.subject | Google cloud platform | en_US |
| dc.title | Learning audio features for genre classification with deep belief networks | en_US |
| dc.type | Project | en_US |