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Regression-Based Tempo Recognition from Chroma and Energy Accents for Slow Audio Recordings

Although the performance of automatic tempo estimation methods has been improved during the recent research activities, some objectives to solve are still remaining. One of them is the analysis of slow music or songs without a strong drum pulse which corresponds to the correct tempo. One of the most frequent errors is the prediction of the doubled tempo, however further error sources exist. In our work we reimplemented, extended and optimized the original tempo recognition method from Eronen and Klapuri with the concrete goal to achieve reliable classification accuracy especially for slow songs. The results from the experiment study confirm the increased quality of the adapted algorithm chain. Several possible error sources are discussed in detail and further ideas beside the scope of this work are proposed for future research.


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