Abstract
In this paper, we address the question of which low-level
acoustical features are the most adequate for identifying music
beats computationally. We consider 172 features computed
on consecutive signal frames and systematically evaluate
their individual value in the task of providing reliable cues
for the presence and localisation of beats in music signals.
We compare two ways of evaluating features: their accuracy
in a song-specific classification task (classifying beats vs nonbeats)
and their performance as a front-end to a beat tracking
system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 32nd International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007) |
| Number of pages | 4 |
| Publication status | Published - 2007 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102015 Information systems
- 202002 Audiovisual media