In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007) , Vienna, Austria.
There are many MIR applications for which we would like
to be able to determine the perceived tempo of a song automatically.
However, automatic tempo extraction itself is
still an open problem. In general there are two tempo extraction
methods, either based on the estimation of interonset
intervals or based on self similarity computations.
To predict a tempo the most significant time-lag or the
most significant inter-onset-interval is used. We propose
to use existing rhythm patterns and reformulate the tempo
extraction problem in terms of a nearest neighbor classification
problem. Our experiments, based on three different
datasets, show that this novel approach performs at
least comparably to state-of-the-art tempo extraction algorithms
and could be useful to get a deeper insight into the
relation between perceived tempo and rhythm patterns.