A sung vocal line is the prominent feature of much popular music. It would be useful to locate the portions of a musical track during which the vocals are present reliably, both as a 'signature' of the piece and as a precursor to automatic recognition of lyrics. We approach this problem by using the acoustic classifier of a speech recognizer as a detector for speech-like sounds. Although singing (including a musical background) is a relatively poor match to an acoustic model trained on normal speech, we propose various statistics of the classifier's output in order to discriminate singing from instrumental accompaniment. A simple HMM allows us to find a best labeling sequence for this uncertain data. On a test set of forty 15 second excerpts of randomly-selected music, our classifier achieved around 80% classification accuracy at the frame level. The utility of different features, and our plans for eventual lyrics recognition, are discussed.
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