New techniques in analysis of dynamic parameters of rail fastening

Smutny, Jaroslav; Pazdera, Lubos

doi:10.1784/insi.46.10.612.45212

Cited by 18 publications

(15 citation statements)

References 3 publications

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“…These methods are extremely important in fields ranging from quantum mechanics (1)(2)(3)(4)(5) to engineering (6,7), animal vocalizations (8,9), radar (10), sound analysis and speech recognition (11)(12)(13), geophysics (14,15), shaped laser pulses (16)(17)(18), the physiology of hearing, and musicography.…”

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confidence: 99%

Sparse time-frequency representations

Gardner

Magnasco²

2006

Proc. Natl. Acad. Sci. U.S.A.

117

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Auditory neurons preserve exquisite temporal information about sound features, but we do not know how the brain uses this information to process the rapidly changing sounds of the natural world. Simple arguments for effective use of temporal information led us to consider the reassignment class of time-frequency representations as a model of auditory processing. Reassigned timefrequency representations can track isolated simple signals with accuracy unlimited by the time-frequency uncertainty principle, but lack of a general theory has hampered their application to complex sounds. We describe the reassigned representations for white noise and show that even spectrally dense signals produce sparse reassignments: the representation collapses onto a thin set of lines arranged in a froth-like pattern. Preserving phase information allows reconstruction of the original signal. We define a notion of ''consensus,'' based on stability of reassignment to time-scale changes, which produces sharp spectral estimates for a wide class of complex mixed signals. As the only currently known class of time-frequency representations that is always ''in focus'' this methodology has general utility in signal analysis. It may also help explain the remarkable acuity of auditory perception. Many details of complex sounds that are virtually undetectable in standard sonograms are readily perceptible and visible in reassignment.auditory ͉ reassignment ͉ spectral ͉ spectrograms ͉ uncertainty T ime-frequency analysis seeks to decompose a onedimensional signal along two dimensions, a time axis and a frequency axis; the best known time-frequency representation is the musical score, which notates frequency vertically and time horizontally. These methods are extremely important in fields ranging from quantum mechanics (1-5) to engineering (6, 7), animal vocalizations (8, 9), radar (10), sound analysis and speech recognition (11-13), geophysics (14, 15), shaped laser pulses (16-18), the physiology of hearing, and musicography. ¶ A central question of auditory theory motivates our study: what algorithms do the brain use to parse the rapidly changing sounds of the natural world? Auditory neurons preserve detailed temporal information about sound features, but we do not know how the brain uses it to process sound. Although it is accepted that the auditory system must perform some type of time-frequency analysis, we do not know which type. The many inequivalent classes of time-frequency distributions (2, 3, 6) require very different kinds of computations: linear transforms include the Gabor transform (19), quadratic transforms [known as Cohen's class (2, 6)] include the Wigner-Ville (1) and Choi-Williams (20) distributions, and higher-order in the signal, include multitapered spectral estimates (21-24), the Hilbert-Huang distribution (25,26), and the reassigned spectrograms (27-32) whose properties are the subject of this article. Results and DiscussionThe auditory nerve preserves information about phases of oscillations much more accurately than info...

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Sparse time-frequency representations

Gardner

Magnasco²

2006

Proc. Natl. Acad. Sci. U.S.A.

117

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“…Signal analysis is mainly performed in the time and frequency domain [1]. In the time domain, the time course of the measured variable is analyzed, or damping analysis in time, the effective value is calculated, etc.…”

Section: The Brief Introduction To Signal Analysis Methodsmentioning

confidence: 99%

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28more detailed view of the characteristics of the tested structures.

THE BRIEF INTRODUCTION TO SIGNAL ANALYSIS METHODSSignal analysis is mainly performed in the time and frequency domain [1]. In the time domain, the time course of the measured variable is analyzed, or damping analysis in time, the effective value is calculated, etc.

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The Use of the Hilbert Huang Transformation in the Testing of Railway Structures

Smutny¹,

Janoštík²,

Nohál³

2021

Akustika

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The aim of the paper is to introduce a less used method for the evaluation of non-stationary and especially transient phenomena in railway structures to the wider professional public. This method may find wide application in many technical and other fields. It is the so-called Hilbert-Huang transform. In this paper, its application in the study of dynamic phenomena occurring in a selected superstructure structure is shown. The calculation procedure of the presented transform differs from traditional tools, which include, for example, the short-term Fourier transform or the Wavelet transform. The paper includes a mathematical analysis and description of this transformation. Furthermore, the paper contains a description of the measurement method used, a discussion of the results obtained and recommendations for practice.

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“…Measures to reduce noise levels by less than 3 dB would, by themselves, be of no real value. Sound can also be transmitted as vibration through the ground and directly into the body, and this is also a form of noise pollution [5,11,12].…”

Section: Effects Of Noisementioning

confidence: 99%

Strategy for tram noise reduction

Panulinová

Bašková

2017

MATEC Web Conf.

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Abstract. When there are many sources it is important to determine the sound power and directivity of each to determine their relative contributions to the noise problem. Any noise problem may be described in terms of a source, a transmission path and a receiver and noise control may take the form of altering any one or all of these elements. The aim of this paper is to suggest ways of reducing tram noise by technical and technological means.

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New techniques in analysis of dynamic parameters of rail fastening

Cited by 18 publications

References 3 publications

Sparse time-frequency representations

Sparse time-frequency representations

The Use of the Hilbert Huang Transformation in the Testing of Railway Structures

Strategy for tram noise reduction

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