Literature DB >> 16454291

Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications.

Sean A Fulop1, Kelly Fitz.   

Abstract

A modification of the spectrogram (log magnitude of the short-time Fourier transform) to more accurately show the instantaneous frequencies of signal components was first proposed in 1976 [Kodera et al., Phys. Earth Planet. Inter. 12, 142-150 (1976)], and has been considered or reinvented a few times since but never widely adopted. This paper presents a unified theoretical picture of this time-frequency analysis method, the time-corrected instantaneous frequency spectrogram, together with detailed implementable algorithms comparing three published techniques for its computation. The new representation is evaluated against the conventional spectrogram for its superior ability to track signal components. The lack of a uniform framework for either mathematics or implementation details which has characterized the disparate literature on the schemes has been remedied here. Fruitful application of the method is shown in the realms of speech phonation analysis, whale song pitch tracking, and additive sound modeling.

Mesh:

Year:  2006        PMID: 16454291     DOI: 10.1121/1.2133000

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  12 in total

1.  Experimental measure of arm stiffness during single reaching movements with a time-frequency analysis.

Authors:  Davide Piovesan; Alberto Pierobon; Paul DiZio; James R Lackner
Journal:  J Neurophysiol       Date:  2013-08-14       Impact factor: 2.714

2.  F0-induced formant measurement errors result in biased variabilities.

Authors:  Wei-Rong Chen; D H Whalen; Christine H Shadle
Journal:  J Acoust Soc Am       Date:  2019-05       Impact factor: 1.840

3.  Acoustic characteristics of phonation in "wet voice" conditions.

Authors:  Shanmugam Murugappan; Suzanne Boyce; Sid Khosla; Lisa Kelchner; Ephraim Gutmark
Journal:  J Acoust Soc Am       Date:  2010-04       Impact factor: 1.840

4.  A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.

Authors:  Zhengshi Yang; Xiaowei Zhuang; Karthik Sreenivasan; Virendra Mishra; Tim Curran; Dietmar Cordes
Journal:  Med Image Anal       Date:  2019-11-26       Impact factor: 8.545

5.  Frequency-Dependent Changes in Resting State Electroencephalogram Functional Networks after Traumatic Brain Injury in Piglets.

Authors:  Lorre S Atlan; Susan S Margulies
Journal:  J Neurotrauma       Date:  2019-05-23       Impact factor: 5.269

6.  Formants are easy to measure; resonances, not so much: Lessons from Klatt (1986).

Authors:  D H Whalen; Wei-Rong Chen; Christine H Shadle; Sean A Fulop
Journal:  J Acoust Soc Am       Date:  2022-08       Impact factor: 2.482

7.  Measuring multi-joint stiffness during single movements: numerical validation of a novel time-frequency approach.

Authors:  Davide Piovesan; Alberto Pierobon; Paul DiZio; James R Lackner
Journal:  PLoS One       Date:  2012-03-20       Impact factor: 3.240

8.  Degraded time-frequency acuity to time-reversed notes.

Authors:  Jacob N Oppenheim; Pavel Isakov; Marcelo O Magnasco
Journal:  PLoS One       Date:  2013-06-17       Impact factor: 3.240

9.  Diagnosing Breast Cancer with Microwave Technology: remaining challenges and potential solutions with machine learning.

Authors:  Bárbara L Oliveira; Daniela Godinho; Martin O'Halloran; Martin Glavin; Edward Jones; Raquel C Conceição
Journal:  Diagnostics (Basel)       Date:  2018-05-19

10.  Female resistance and harmonic convergence influence male mating success in Aedes aegypti.

Authors:  Andrew Aldersley; Lauren J Cator
Journal:  Sci Rep       Date:  2019-02-14       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.