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Journal ArticleDOI

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

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TLDR
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 are presented.
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.

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Citations
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Journal ArticleDOI

Time-Frequency Reassignment and Synchrosqueezing: An Overview

TL;DR: This article provides a general overview of time-frequency (T-F) reassignment and synchrosqueezing techniques applied to multicomponent signals, covering the theoretical background and applications.
Journal ArticleDOI

The Synchrosqueezing algorithm for time-varying spectral analysis

TL;DR: It is shown that Synchrosqueezing is robust to bounded perturbations of the signal and to Gaussian white noise, which justifies its applicability to noisy or nonuniformly sampled data that is ubiquitous in engineering and the natural sciences.
Journal ArticleDOI

Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

TL;DR: A novel framework for SER is introduced using a key sequence segment selection based on redial based function network (RBFN) similarity measurement in clusters to reduce the computational complexity of the overall model and normalize the CNN features before their actual processing, so that it can easily recognize the Spatio-temporal information.
Book

An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics

TL;DR: This book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike.
Journal ArticleDOI

Doppler-based detection and tracking of humans in indoor environments

TL;DR: The topics discussed include the micro-Doppler characteristics of humans, the azimuth, elevation and range tracking of humans using Doppler, spatial and frequency diversity, the effect of walls, and the characteristics of dynamic clutters from non-humans.
References
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Theory of communication

Dennis Gabor
Journal ArticleDOI

Localization of the complex spectrum: the S transform

TL;DR: The S transform is shown to have some desirable characteristics that are absent in the continuous wavelet transform, and provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.
Journal Article

Localisation of the complex spectrum : The S transform

TL;DR: The S transform as discussed by the authors is an extension to the ideas of the Gabor transform and the Wavelet transform, based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms.
Journal ArticleDOI

Improving the readability of time-frequency and time-scale representations by the reassignment method

TL;DR: The reassignment method, first applied by Kodera, Gendrin, and de Villedary (1976) to the spectrogram, is generalized to any bilinear time-frequency or time-scale distribution.
Journal ArticleDOI

Signal energy distribution in time and frequency

TL;DR: It is shown in this paper that the studies of Fourier representation of signals and its relation to the signal structure in time and frequency can be unified by the introduction of the concept of the complex energy density function of a signal.
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