Abstract
Audio-to-audio alignment is the task of synchronizing two audio sequences with similar musical content in time. We investigated a large set of audio features for this task. The features were chosen to represent four different content-dependent similarity categories: the envelope, the timbre, note-onsets and the pitch. The features were subjected to two processing stages. First, a feature subset was selected by evaluating the alignment performance of each individual feature. Second, the selected features were combined and subjected to an automatic weighting algorithm. A new method for the objective evaluation of audio-to-audio alignment systems is proposed that enables the use of arbitrary kinds of music as ground truth data. We evaluated our algorithm by this method as well as on a data set of real recordings of solo piano music. The results showed that the feature weighting algorithm could improve the alignment accuracies compared to the results of the individual features.
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