A hybrid approach to singing pitch extraction based on trend estimation and hidden Markov models
| Autor: | I-Bin Liao, Wei-Lun Chang, Jyh-Shing Roger Jang, Ming-Ju Wu, Tzu-Chun Yeh |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.
HCI) Computer science business.industry Speech recognition Pitch detection algorithm Pattern recognition Tracking (particle physics) computer.software_genre Polyphony Artificial intelligence Singing business Hidden Markov model Pitch tracking Audio signal processing Trend estimation computer |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp.2012.6287915 |
| Popis: | In this paper, we propose a hybrid method for singing pitch extraction from polyphonic audio music. We have observed several kinds of pitch errors made by a previously proposed algorithm based on trend estimation. We also noticed that other pitch tracking methods tend to have other types of pitch error. Then it becomes intuitive to combine the results of several pitch trackers to achieve a better accuracy. In this paper, we adopt 3 methods as a committee to determine the pitch, including the trend-estimation-based method for forward and backward signals, and training-based HMM method. Experimental results demonstrate that the proposed approach outperforms the best algorithm for the task of audio melody extraction in MIREX 2010. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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