Sparse Bayesian learning with multiple dictionaries

Nannuru, Santosh; Gemba, Kay L.; Gerstoft, Peter; Hodgkiss, William S.; Mecklenbräuker, Christoph F.

doi:10.1016/j.sigpro.2019.02.003

Cited by 66 publications

(61 citation statements)

References 49 publications

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“…In acoustics this framework has been used for range estimation 30 and for sparse estimation via the sparse Bayesian learning approach. 31,32 In the latter, the sparsity is controlled by diagonal prior covariance matrix, where entries with zero prior variance will force the posterior variance and mean to be zero. With prior knowledge and assumptions about the data, Bayesian approaches to parameter estimation can prevent overfitting.…”

Section: F Bayesian Machine Learningmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

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424

126

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Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.

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Section: F Bayesian Machine Learningmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

Self Cite

424

126

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“…This update rule was used in [42] . Using an exponent of 1 (instead of 0.5) in (36) gives the update equation used in [39,43,57] .…”

Section: Source Power Estimationmentioning

confidence: 99%

“…Specifically, we derive noise variance estimates and demonstrate this for compressive beamforming [37][38][39][40][41] using multiple measurement vectors (MMV), also called multiple snapshots. We solve the MMV problem using the sparse Bayesian learning (SBL) framework [39,42,43] . We assume the source signals to jointly follow a zero-mean multivariate complex normal distribution with unknown power levels.…”

Section: Introductionmentioning

confidence: 99%

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DOA estimation in heteroscedastic noise

Gerstoft

Gemba

Nannuru

2018

The Journal of the Acoustical Society of America

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The paper considers direction of arrival (DOA) estimation from long-term observations in a very noisy environment. The concern is to derive methods obtaining reasonable DOAs at very low SNR. The noise is assumed zero-mean Gaussian and its variance varies in time and space, causing stationary data models to fit poorly over long observation times. Therefore a heteroscedastic Gaussian noise model is introduced where the variance varies across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), inspiring stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. This SBL approach is more flexible and performs better than other high-resolution methods since they cannot estimate the heteroscedastic noise process. An alternative to SBL is simple data normalization, whereby only the phase across the array is utilized. Simulations in noisy environments demonstrate that taking the heteroscedastic noise into account causes the DOA estimation to fail at lower SNR, often at 20 dB lower SNR.

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“…Second, the SBL procedure has been shown to be robust to undesirable dictionary structure such as the combination of high coherence 2 and diverse column magnitudes [13], structure that violates sufficient conditions for 1 -based sparse estimation algorithms but that nonetheless commonly appears in real-world applications such as imaging [14]. Third, many applications make use of the SBL framework because its probabilistic framework allows other application-specific priors or constraints to be incorporated into the inference process in a simple and interpretable way (e.g., [15]). Finally, the SBL inference procedure allows the noise variance (sparsity) parameter to be learned automatically, reducing the need for manual parameter tuning compared with similar algorithms.…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals

O’Shaughnessy

Davenport

Rozell

2020

IEEE Trans. Signal Process.

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Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an algorithm for dynamic filtering of time-varying sparse signals based on the sparse Bayesian learning (SBL) framework. The key idea underlying the algorithm, termed SBL-DF, is the incorporation of a signal prediction generated from a dynamics model and estimates of previous time steps into the hyperpriors of the SBL probability model. The proposed algorithm is online, robust to imperfect dynamics models (due to the propagation of dynamics information through higher-order statistics), robust to certain undesirable dictionary properties such as coherence (due to properties of the SBL framework), allows the use of arbitrary dynamics models, and requires the tuning of fewer parameters than many other dynamic filtering algorithms do. We also extend the fast marginal likelihood SBL inference procedure to the informative hyperprior setting to create a particularly efficient version of the SBL-DF algorithm. Numerical simulations show that SBL-DF converges much faster and to more accurate solutions than standard SBL and other dynamical filtering algorithms. In particular, we show that SBL-DF outperforms state of the art algorithms when the dictionary contains the challenging coherence and column scaling structure found in many practical applications.

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Sparse Bayesian learning with multiple dictionaries

Cited by 66 publications

References 49 publications

Machine learning in acoustics: Theory and applications

Machine learning in acoustics: Theory and applications

DOA estimation in heteroscedastic noise

Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals

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