Improving Music Genre Classification Using Automatically Induced Harmony Rules
Bibliographic Data
| ID | 5304378 |
|---|---|
| Authors | Amélie Anglade (Queen Mary University of London, corresponding author), E Benetos (0000-0002-6820-6764), Matthias Mauch (0000-0002-4352-6809, National Institute of Advanced Industrial Science and Technology), Simon Dixon (0000-0002-6098-481X, Queen Mary University of London) |
| Year | 2010 |
| Volume | 39 |
| Issue | 4 |
| Pages | 349-361 |
| Publication date | 2010-12-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of New Music Research (JOURNAL) |
| Journal identifiers | ISSN: 0929-8215 • E-ISSN: 1744-5027 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09298215.2010.525654 |
| OpenAlex | W2061403897 |
| Language | EN |
| Citations received | 1 |
| References cited | 20 |
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates
Art · Feature vector · Harmony search · Machine learning · Natural language processing · Random forest · Speech recognition · Support vector machine · Computer Science · Music and Audio Processing · Music Technology and Sound Studies · Speech and Audio Processing · Artificial Intelligence
| Citations per year | 0.06 |
|---|---|
| Citation span | 2010 - 2010 (1) |
| Highly cited | No |