2011
DOI: 10.1109/tasl.2010.2089517
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Distinguishing Monophonies From Polyphonies Using Weibull Bivariate Distributions

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Cited by 6 publications
(11 citation statements)
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“…As indicated in Table 3, classification results are near perfect, ensuring robust mode identification during performance. In addition, we have developed an audio-based approach that uses the Yin pitch-detection algorithm [11]. Yin is used to extract the pitch and confidence indicator (CI) on a window of 50ms.…”
Section: Chord-dur-ratiomentioning
confidence: 99%
“…As indicated in Table 3, classification results are near perfect, ensuring robust mode identification during performance. In addition, we have developed an audio-based approach that uses the Yin pitch-detection algorithm [11]. Yin is used to extract the pitch and confidence indicator (CI) on a window of 50ms.…”
Section: Chord-dur-ratiomentioning
confidence: 99%
“…The popularity of the histogram in many areas of engineering, such as image processing [7][8]16], medical imaging [17][18][19][20], remote sensing [21], audio processing [22] and ADC testing [23], lies in the fact that it provides a visual approximation of almost any density shape f(x) without extensive statistical analysis [4].…”
Section: Histogram the Classical Density Estimatormentioning
confidence: 99%
“…Using the work of Lachambre [3], we perform an estimation on whether the whole music segments are monophonic or polyphonic. This step can be considered as a conjoint information more than a real pre-requite for the multiple source localisation as it can be refined by the latter methods.…”
Section: Monophonic-polyphonicmentioning
confidence: 99%