Blind Signal Separation for Digital Communication Data

Chevreuil, Antoine; Loubaton, Philippe

doi:10.1016/b978-0-12-396500-4.00004-1

Cited by 6 publications

(2 citation statements)

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“…Source separation is a problem of fundamental importance in the field of signal processing, with a wide range of applications in various domains such as telecommunications (Chevreuil & Loubaton, 2014;Gay & Benesty, 2012;Khosravy et al, 2020), speech processing (Pedersen et al, 2008;Chua et al, 2016;Grais et al, 2014), biomedical signal processing (Adali et al, 2015;Barriga et al, 2003;Hasan et al, 2018) and geophysical data processing (Ibrahim & Sacchi, 2014;Kumar et al, 2015;Scholz et al, 2020). 1 Rice University 2 École Normale Supérieure 3 Institut de Physique du Globe de Paris.…”

Section: Introductionmentioning

confidence: 99%

Unearthing InSights into Mars: unsupervised source separation with limited data

Siahkoohi¹,

Rudy²,

Hoop³

et al. 2023

Preprint

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Source separation entails the ill-posed problem of retrieving a set of source signals observed through a mixing operator. Solving this problem requires prior knowledge, which is commonly incorporated by imposing regularity conditions on the source signals or implicitly learned in supervised or unsupervised methods from existing data. While data-driven methods have shown great promise in source separation, they are often dependent on large amounts of data, which rarely exists in planetary space missions. Considering this challenge, we propose an unsupervised source separation scheme for domains with limited data access that involves solving an optimization problem in the wavelet scattering representation space-an interpretable low-dimensional representation of stationary processes. We present a real-data example in which we remove transient thermally induced microtilts, known as glitches, from data recorded by a seismometer during NASA's InSight mission on Mars. Owing to the wavelet scattering covariances' ability to capture non-Gaussian properties of stochastic processes, we are able to separate glitches using only a few glitch-free data snippets.

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Section: Introductionmentioning

confidence: 99%

Unearthing InSights into Mars: unsupervised source separation with limited data

Siahkoohi¹,

Rudy²,

Hoop³

et al. 2023

Preprint

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“…It has been widely used in many different fields. These applications include but are not limited to image [88,117], biomedical [74,77,115], Sonar [38,44], seismic [103,173], telecommunications [31,179], and audio [106,190] applications. In the thesis, we are interested in acoustics applications, and hence in the remaining of the chapter all the discussions and the derivations are based on acoustics implementations.…”

Section: Possible Bss Applicationsmentioning

confidence: 99%

Low Latency Convolutive Blind Source Separation

Chua¹

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<p>In most real-time systems, particularly for applications involving system identification, latency is a critical issue. These applications include, but are not limited to, blind source separation (BSS), beamforming, speech dereverberation, acoustic echo cancellation and channel equalization. The system latency consists of an algorithmic delay and an estimation computational time. The latter can be avoided by using a multi-thread system, which runs the estimation process and the processing procedure simultaneously. The former, which consists of a delay of one window length, is usually unavoidable for the frequency-domain approaches. For frequency-domain approaches, a block of data is acquired by using a window, transformed and processed in the frequency domain, and recovered back to the time domain by using an overlap-add technique. In the frequency domain, the convolutive model, which is usually used to describe the process of a linear time-invariant (LTI) system, can be represented by a series of multiplicative models to facilitate estimation. To implement frequency-domain approaches in real-time applications, the short-time Fourier transform (STFT) is commonly used. The window used in the STFT must be at least twice the room impulse response which is long, so that the multiplicative model is sufficiently accurate. The delay constraint caused by the associated blockwise processing window length makes most the frequency-domain approaches inapplicable for real-time systems. This thesis aims to design a BSS system that can be used in a real-time scenario with minimal latency. Existing BSS approaches can be integrated into our system to perform source separation with low delay without affecting the separation performance. The second goal is to design a BSS system that can perform source separation in a non-stationary environment. We first introduce a subspace approach to directly estimate the separation parameters in the low-frequency-resolution time-frequency (LFRTF) domain. In the LFRTF domain, a shorter window is used to reduce the algorithmic delay of the system during the signal acquisition, e.g., the window length is shorter than the room impulse response. The subspace method facilitates the deconvolution of a convolutive mixture to a new instantaneous mixture and simplifies the estimation process. Second, we propose an alternative approach to address the algorithmic latency problem. The alternative method enables us to obtain the separation parameters in the LFRTF domain based on parameters estimated in the high-frequency-resolution time-frequency (HFRTF) domain, where the window length is longer than the room impulse response, without affecting the separation performance. The thesis also provides a solution to address the BSS problem in a non-stationary environment. We utilize the ``meta-information" that is obtained from previous BSS operations to facilitate the separation in the future without performing the entire BSS process again. Repeating a BSS process can be computationally expensive. Most conventional BSS algorithms require sufficient signal samples to perform analysis and this prolongs the estimation delay. By utilizing information from the entire spectrum, our method enables us to update the separation parameters with only a single snapshot of observation data. Hence, our method minimizes the estimation period, reduces the redundancy and improves the efficacy of the system. The final contribution of the thesis is a non-iterative method for impulse response shortening. This method allows us to use a shorter representation to approximate the long impulse response. It further improves the computational efficiency of the algorithm and yet achieves satisfactory performance.</p>

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Introduction to Signal Processing for Communications

Sidiropoulos¹

2014

Academic Press Library in Signal Processing: Volume 2 - Communications and Radar Signal Processing

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Blind Signal Separation for Digital Communication Data

Cited by 6 publications

References 57 publications

Unearthing InSights into Mars: unsupervised source separation with limited data

Unearthing InSights into Mars: unsupervised source separation with limited data

Low Latency Convolutive Blind Source Separation

Introduction to Signal Processing for Communications

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