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The shape of an instant: Measuring a...

Stanford University.

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The shape of an instant: Measuring and modeling perceptual attack time with probability density functions (if a tree falls in the forest, when did 57 people hear it make a sound?).

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The shape of an instant: Measuring and modeling perceptual attack time with probability density functions (if a tree falls in the forest, when did 57 people hear it make a sound?)./
作者:	Wright, Matthew James.
面頁冊數:	188 p.
附註:	Adviser: Chris Chafe.
Contained By:	Dissertation Abstracts International69-02A.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3302886
ISBN:	9780549489351

The shape of an instant: Measuring and modeling perceptual attack time with probability density functions (if a tree falls in the forest, when did 57 people hear it make a sound?).
Wright, Matthew James.

The shape of an instant: Measuring and modeling perceptual attack time with probability density functions (if a tree falls in the forest, when did 57 people hear it make a sound?). - 188 p.

Adviser: Chris Chafe.

Thesis (Ph.D.)--Stanford University, 2008.

A sound's Perceptual Attack Time ("PAT") is its perceived moment of rhythmic placement. PAT is notoriously difficult to measure, because all methods measure a test sound's PAT in relationship to an action or second sound, which add uncertainty to the measurements. This work uses the ideal impulse, which is an ideal reference because it is perfectly isolated in time with a very clear attack. However, its perfectly broad frequency spectrum is problematic because it is harder to perceive the relative timing of sounds when their spectra differ greatly. This motivates Spectrally Matched Click Synthesis, the creation of arbitrarily short clicks whose spectra approximate those of arbitrary input sounds.

ISBN: 9780549489351Subjects--Topical Terms:

626642
Computer Science.

The shape of an instant: Measuring and modeling perceptual attack time with probability density functions (if a tree falls in the forest, when did 57 people hear it make a sound?).
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520 $a A sound's Perceptual Attack Time ("PAT") is its perceived moment of rhythmic placement. PAT is notoriously difficult to measure, because all methods measure a test sound's PAT in relationship to an action or second sound, which add uncertainty to the measurements. This work uses the ideal impulse, which is an ideal reference because it is perfectly isolated in time with a very clear attack. However, its perfectly broad frequency spectrum is problematic because it is harder to perceive the relative timing of sounds when their spectra differ greatly. This motivates Spectrally Matched Click Synthesis, the creation of arbitrarily short clicks whose spectra approximate those of arbitrary input sounds.
520 $a This work represents PAT as a function indicating the probability that a typical listener would hear the sound's PAT at each time, rather than as just one instant. The problem of deriving each sound's PAT from pairwise comparisons becomes one of estimating distributions of random variables for every sound's PAT given only observations of random variables corresponding to the difference two sounds' distributions plus noise. Methods to address this draw from maximum likelihood estimation and the shortest path problem.
520 $a Subjects downloaded a listening test that presents a series of PAT measurement trials and allows them to adjust sounds' relative timing until they sound synchronous. This establishes perceptual "ground truth" for the PAT of a collection of 20 sounds compared against each other in various combinations. As hoped, subjects were able to align a sound more reliably to one of that sound's spectrally matched clicks than to other sounds of the same duration.
520 $a The representation of PAT with probability density functions provides a new perspective on the problem of predicting PAT directly from acoustical signals. Rather than choosing a single moment for PAT given a segment of sound known a priori to contain a single musical event, these regression methods estimate continuous shapes of PAT distributions from continuous (not necessarily presegmented) audio signals, formulated as a supervised machine learning regression problem whose inputs are DSP functions computed from the sound. This work concludes with some preliminary musical applications of the resulting models.
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