Fractionally addressed delay lines

Rocchesso, Davide

doi:10.1109/89.876310

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…The resampling process requires suitable interpolation as access to samples at fractional delays is required. Here, a Lagrange-Interpolation [13] is used to determine the emission sample e at arbitrary time n þ s where n is an integer and s is the fraction as…”

Section: Adjustment Of Rotational Speedmentioning

confidence: 99%

Synthesis of real world drone signals based on lab recordings

Heutschi

Ott²,

Nußbaumer

et al. 2020

Acta Acust.

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There is a great interest in the generation of plausible drone signals in various applications, e.g. for auralization purposes or the compilation of training data for detection algorithms. Here, a methodology is presented which synthesises realistic immission signals based on laboratory recordings and subsequent signal processing. The transformation of a lab drone signal into a virtual field microphone signal has to consider a constant pitch shift to adjust for the manoeuvre specific rotational speed and the corresponding frequency dependent emission strength correction, a random pitch shift variation to account for turbulence induced rotational speed variations in the field, Doppler frequency shift and time and frequency dependent amplitude adjustments according to the different propagation effects. By evaluation of lab and field measurements, the relevant synthesizer parameters were determined. It was found that for the investigated set of drone types, the vertical radiation characteristics can be successfully described by a generic frequency dependent directivity pattern. The proposed method is applied to different drone models with a total weight between 800 g and 3.4 kg and is discussed with respect to its abilities and limitations comparing both, recordings taken in the lab and the field.

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Section: Adjustment Of Rotational Speedmentioning

confidence: 99%

Synthesis of real world drone signals based on lab recordings

Heutschi

Ott²,

Nußbaumer

et al. 2020

Acta Acust.

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“…After adjusting delay times to achieve a spectrally well-balanced result from the lossless prototype, we then apply additional filters and decay coefficients to achieve the desired decay time and spectral envelope 3 . Hence to ensure that these filters produce the intended effect, the sound of the unfiltered lossless FDN should resemble white noise as closely as possible [8][9][10][11] . a) natalie agus@mymail.sutd.edu.sg…”

Section: Introductionmentioning

confidence: 99%

Perceptual evaluation of measures of spectral variance

Agus

Anderson

Chen

et al. 2018

The Journal of the Acoustical Society of America

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In many applications, it is desirable to achieve a signal that is as close as possible to ideal white noise. One example is in the design of an artificial reverberator, whereby there is a need for its lossless prototype output from an impulse input to be perceptually white as much as possible. The Ljung-Box test, the Drouiche test, and the Wiener Entropy-also called the Spectral Flatness Measure-are three well-known methods for quantifying the similarity of a given signal to ideal white noise. In this paper, listening tests are conducted to measure the Just Noticeable Difference (JND) on the perception of white noise, which is the JND between ideal Gaussian white noise and noise with a specified deviation from the flat spectrum. This paper reports the JND values using one of these measures of whiteness, which is the Ljung-Box test. This paper finds considerable disagreement between the Ljung-Box test and the other two methods and shows that none of the methods is a significantly better predictor of listeners' perception of whiteness. This suggests a need for a whiteness test that is more closely correlated to human perception.

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