Note:
This bibliographic page is archived and will no longer be updated.
For an up-to-date list of publications from the Music Technology Group see the
Publications list
.
Mining Melodic Patterns in Large Audio Collections of Indian Art Music
| Title | Mining Melodic Patterns in Large Audio Collections of Indian Art Music |
| Publication Type | Conference Paper |
| Year of Publication | 2014 |
| Conference Name | International Conference on Signal Image Technology & Internet Based Systems - Multimedia Information Retrieval and Applications |
| Authors | Gulati, S. , Serrà J. , Ishwar V. , & Serra X. |
| Pagination | 264-271 |
| Conference Start Date | 23/11/2014 |
| Conference Location | Marrakesh, Morocco |
| Abstract | Discovery of repeating structures in music is fundamental to its analysis, understanding and interpretation. We present a data-driven approach for the discovery of short-time melodic patterns in large collections of Indian art music. The approach first discovers melodic patterns within an audio recording and subsequently searches for their repetitions in the entire music collection. We compute similarity between melodic patterns using dynamic time warping (DTW). Furthermore, we investigate four different variants of the DTW cost function for rank refinement of the obtained results. The music collection used in this study comprises 1,764 audio recordings with a total duration of 365 hours. Over 13 trillion DTW distance computations are done for the entire dataset. Due to the computational complexity of the task, different lower bounding and early abandoning techniques are applied during DTW distance computation. An evaluation based on expert feedback on a subset of the dataset shows that the discovered melodic patterns are musically relevant. Several musically interesting relationships are discovered, yielding further scope for establishing novel similarity measures based on melodic patterns. The discovered melodic patterns can further be used in challenging computational tasks such as automatic raga recognition, composition identification and music recommendation. |
| preprint/postprint document | http://hdl.handle.net/10230/35757 |
Additional material:
To access shared resources for this article visit its companion webpage at:
http://compmusic.upf.edu/node/210
