Name
Abstract | ||
|---|---|---|
Leveraging on the strength of energy-based processing for transient detection and pitch-based processing for softer onsets detection, we present a system that combines both energy and pitch cues for detecting onsets from different instrument categories. Given an audio input from an arbitrary instrument category, the system performs preliminary onset categorization based on the general note characteristics and then performs onsets integration based on the categorization. In addition, the proposed pitch processing technique explores musically relevant features extracted from the chromagram, which are robust for detecting pitch changes. The proposed system showed good detection performance on the MIREX audio onset detection dataset. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ISCAS.2010.5537762 | ISCAS |
Keywords | Field | DocType |
mirex audio onset detection dataset,energy-based processing,chromagram,pitch-based processing,feature extraction,audio signal processing,musical instrument,signal detection,transient detection,musically relevant feature extraction,tuning,estimation,harmonic analysis,system performance | Categorization,Detection theory,Computer science,Musical instrument,Feature extraction,Speech recognition,Harmonic analysis,Transient analysis,Audio signal processing | Conference |
ISSN | ISBN | Citations |
0271-4302 | 978-1-4244-5309-2 | 4 |
PageRank | References | Authors |
0.52 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Li Tan | 1 | 76 | 7.42 |
| Yongwei Zhu | 2 | 104 | 14.68 |
| Lekha Chaisorn | 3 | 279 | 26.07 |
| Susanto Rahardja | 4 | 652 | 102.05 |