Recursive versions of the Levenberg-Marquardt reassigned spectrogram and of the synchrosqueezed STFT

2019 27th European Signal Processing Conference (EUSIPCO)

Auger

2019

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This paper addresses the problem of efficiently jointly representing a nonstationary multicomponent signal in time and frequency. We introduce a novel enhancement of the time-reassigned synchrosqueezing method designed to compute sharpened and reversible representations of impulsive or strongly modulated signals. After establishing theoretical relations of the new proposed method with our previous results, we illustrate in numerical experiments the improvement brought by our proposal when applied on both synthetic and real-world signals. Our experiments deal with an analysis of the Draupner wave record for which we provide pioneered time-frequency analysis results.

Section: Analysis Of a Synthetic Signalmentioning

confidence: 99%

Second-order Time-Reassigned Synchrosqueezing Transform: Application to Draupner Wave Analysis

2019 27th European Signal Processing Conference (EUSIPCO)

Auger

2019

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“…z * being the complex conjugate of z and j 2 = −1. Since |X h (t, ω)| 2 provides a TFR called spectrogram, a signal reconstruction can be provided using [12]:…”

Section: The Recursive Levenberg-marquardt Synchrosqueezing Transformmentioning

confidence: 99%

“…The synchrosqueezing transform [7,8] was introduced as a sharpening method which contrarily to the reassignment [9], provides reversible TFRs. This method was recently extended to frequency modulated signals [10,11] and was com-This research has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement n o 688122. bined with the Levenberg-Marquardt algorithm [12] to become adaptive thanks to a damping parameter. These methods have shown their interest for disentangling multicomponent signals or to obtain physically interpretable components [13].…”

Section: Introductionmentioning

confidence: 99%

Fast and Adaptive Blind Audio Source Separation Using Recursive Levenberg-Marquardt Synchrosqueezing

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Peeters

2018

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To cite this version:Dominique Fourer, Geoffroy Peeters. Fast and adaptive blind audio source separation using recursive Levenberg-Marquardt synchrosqueezing.This paper revisits the Degenerate Unmixing Estimation Technique (DUET) for blind audio separation of an arbitrary number of sources given two mixtures through a recursively computed and adaptive time-frequency representation. Recently, synchrosqueezing was introduced as a promising signal disentangling method which allows to compute reversible and sharpen time-frequency representations. Thus, it can be used to reduce overlaps between the sources in the time-frequency plane and to improve the sources' sparsity which is often exploited by source separation techniques. Furthermore, synchrosqueezing can also be extended using the Levenberg-Marquardt algorithm to allow a user to adjust the energy concentration of a time-frequency representation which can be efficiently implemented without the FFT algorithm. Hence, we show that our approach can improve the quality of the source separation process while remaining suitable for real-time applications.

“…An efficient recursive implementation (suitable for real-time computation), is proposed in [10], [12]. It uses a specific causal (one-sided) analysis window which can be expressed as h k (t)=…”

Section: A Short-time Fourier Transform (Stft)mentioning

confidence: 99%

“…1), designed for automatic signal mode extraction, can be viewed as an extension of the previously proposed Time-Frequency ToolBox (TFTB) 1 . It contains implementations of some recent contributions related to reassignment, synchrosqueezing [10], [11], [6], [12], EMD [13], [14], and SSA [9] methods. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

The ASTRES toolbox for mode extraction of non-stationary multicomponent signals

2017 25th European Signal Processing Conference (EUSIPCO)

Harmouche

Schmitt³

et al. 2017

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Abstract-In this paper, we introduce the ASTRES * toolbox which offers a set of Matlab functions for non-stationary multicomponent signal processing. The main purposes of this proposal is to offer efficient tools for analysis, synthesis and transformation of any signal made of physically meaningful components (e.g. sinusoid, trend or noise). The proposed techniques contain some recent and new contributions, which are now unified and theoretically strengthened. They can provide efficient time-frequency or time-scale representations and they allow elementary components extraction. Usage and description of each method are then detailed and numerically illustrated.