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Improving Rhythmic Pattern Features Based on Logarithmic Preprocessing
In the area of Music Information Retrieval, the rhythmic analysis of music plays an important role. In order to derive rhythmic information from music signals, several feature extraction algorithms have been described in the literature. Most of them extract the rhythmic information by auto-correlating the temporal envelope derived from different frequency bands of the music signal. Using the auto-correlated envelopes directly as an audio-feature is afflicted with the disadvantage of tempo dependency. To circumvent this problem, further post-processing via higher-order statistics has been proposed. However, the resulting statistical features are still tempo dependent to a certain extent. This paper describes a novel method, which logarithmizes the lag-axis of the auto-correlated envelope and discards the tempo-dependent part. This approach leads to tempo-invariant rhythmic features. A quantitative comparison of the original methods versus the proposed procedure is described and discussed in this paper.
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