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| In [[signal processing]], the '''high frequency content measure''' is a simple measure, taken across a signal [[spectrum]] (usually a [[STFT]] spectrum), that can be used to characterize the amount of high-frequency content in the signal. The magnitudes of the spectral bins are added together, but multiplying each magnitude by the bin "position" (proportional to the frequency). Thus if ''X''(''k'') is a discrete spectrum with ''N'' unique points, its high frequency content measure is:
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| : <math>
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| \mathrm{HFC} = \sum_{i=0}^{N-1} i|X(i)|
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| </math> | |
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| In contrast to perceptual measures, this is not based on any evidence about its relevance to [[human hearing]]. Despite that, it can be useful for some applications, such as [[onset detection]].
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| The measure has close similarities to the [[spectral centroid]] measure, being essentially the same calculation but without normalization according to overall magnitude.
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| ==References==
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| * P. Brossier, J. P. Bello and M. D. Plumbley. ''[http://aubio.piem.org/articles/brossier04realtimesegmentation.pdf Real-time temporal segmentation of note objects in music signals]'', in Proceedings of the International Computer Music Conference (ICMC 2004), Miami, Florida, USA, November 1–6, 2004.
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| * Masri, P. (1996). ''Computer modeling of Sound for Transformation and Synthesis of Musical Signal.'' PhD dissertation, University of Bristol.
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| [[Category:Digital signal processing]]
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| {{Signal-processing-stub}}
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Revision as of 15:21, 6 February 2014
Nice to meet you, I am Marvella Shryock. North Dakota is her beginning place but she will have to move one working day or another. To do aerobics is a thing that I'm completely addicted to. He used to be unemployed but now he is a pc operator but his promotion by no means comes.
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