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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Benetos, E.; Dixon, S. (2011)
Publisher: IEEE
Languages: English
Types: Unknown
Subjects: M1, QA76

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
In this paper, an approach for polyphonic music transcription based on joint multiple-F0 estimation and note onset/offset detection is proposed. For preprocessing, the resonator time-frequency image of the input music signal is extracted and noise suppression is performed. A pitch salience function is extracted for each frame along with tuning and inharmonicity parameters. For onset detection, late fusion is employed by combining a novel spectral flux-based feature which incorporates pitch tuning information and a novel salience function-based descriptor. For each segment defined by two onsets, an overlapping partial treatment procedure is used and a pitch set score function is proposed. A note offset detection procedure is also proposed using HMMs trained on MIDI data. The system was trained on piano chords and tested on classic and jazz recordings from the RWC database. Improved transcription results are reported compared to state-of-the-art approaches.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Etot 40.3% 38.8%
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