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The number of r played a crucial role in extracting note events.
Many real-time musical signal-processing applications depend on the temporal segmentation of the audio signal into discrete note events.
Systems such as score followers [1] may use detected note events to interact directly with a live performer.
The results were evaluated based on the standard precision and recall measures where, in each frame, true positive tp is the number of correctly transcribed note events, false positive fp is the number of spurious note events and false negative fn is the number of note events that are undetected.
When dealing with framewise classification of musical signals, it must be noted that there is a strong correlation between the classes of adjacent frames, because there is a good chance that no new note events happened between these frames.
Additionally, Smaragdis [103] observed that note events that are never found separately (notes that are never out of a chord) are not recognized by the system, and, at the same time, some spurious events are recognized.
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Editor's Note: Event Partnerships Manager Leslie Hitchcock is a non-editorial TechCrunch employee.
Editor's Note: Event Partnerships Manager Leslie Hitchcock is a non-editorial TechCrunch employee.
cAs reported in [15]: "A note event is counted as correct if the transcribed and the real note do overlap".
As Callen et al (2009, 1364) note, event study analyses provide more direct evidence regarding the information conveyed of earnings disclosures.
Unless noted, events are free; programs are subject to change.
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