An onset detection algorithm for query by humming (QBH) applications using psychoacoustic knowledge

We propose a new algorithm for onset detection in hummed queries for QBH applications. The algorithm uses a modified version of a popular loudness model for human hearing to identify onsets in hums. We also propose the use of a local minimum function to identify onsets better. A sub-band based sone scale processing that is advantageous for a simple implementation is used. On an annotated database of syllabic and natural hums, the algorithm identifies onsets correctly on an average 90% of the time with only 6% false positives. The features used in this algorithm can be used in conjunction with other feature / decision fusion based onset detection systems.