Music Tempo Estimation With $k$-NN Regression
Eronen, A.J.; Klapuri, A.P.
Audio, Speech, and Language Processing, IEEE Transactions on
Volume 18, Issue 1, Jan. 2010 Page(s):50 - 57
Digital Object Identifier   10.1109/TASL.2009.2023165
Summary:An approach for tempo estimation from musical pieces with near-constant tempo is proposed. The method consists of three main steps: measuring the degree of musical accent as a function of time, periodicity analysis, and tempo estimation. Novel accent features based on the chroma representation are proposed. The periodicity of the accent signal is measured using the generalized autocorrelation function, followed by tempo estimation using k-Nearest Neighbor regression. We propose a resampling step applied to an unknown periodicity vector before finding the nearest neighbors. This step improves the performance of the method significantly. The tempo estimate is computed as a distance-weighted median of the nearest neighbor tempi. Experimental results show that the proposed method provides significantly better tempo estimation accuracies than three reference methods.

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