Many state-of-the-art query-by-humming (QBH) systems restrict the hummed query to be in isolated syllables for easy note segmentation. However, it is observed that users often prefer natural humming. This work addresses note onset detection for natural hum, usually considered difficult to segment. The acoustic characteristics of naturally hummed signals are studied and features useful to note onset detection are proposed. Pitch and energy features are combined to obtain superior note segmentation. Performance results on note onset detection as well as retrieval in an actual QBH system are reported.

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