Angle of Arrival Estimation for Broadband Signals: A Comparison

Alrmah, Mohamed Abubaker; Weiss, Stephan; Redif, Soydan; Lambotharan, Sangarapillai; McWhirter, J.G.

doi:10.1049/cp.2013.2066

Cited by 9 publications

(11 citation statements)

References 10 publications

(21 reference statements)

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“…where R x k x k (τ ) is the space-time covariance matrix obtained following (5) in the k th frame of length L, such that n ∈ {(k − 1)L, . .…”

Section: Batch Mode Versus Frame-based Enhancementmentioning

confidence: 99%

“…The topic of polynomial eigenvalue decomposition (PEVD) has recently gained traction in the signal processing literature. Applications were found in multichannel enhancement for arbitrarily-shaped arrays [1], spherical microphones [2], or distributed microphone networks [3]; in channel identification [4]; in DOA estimation with polynomial MUSIC [5][6][7]; in voice activity detection [8]; or in beamforming with a broadband MVDR beamformer [9]. These methods rely on the estimation of a space-time covariance matrix capturing signal correlations in space, time, and frequency, thereby allowing true broadband processing [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Frame-Based Space-Time Covariance Matrix Estimation for Polynomial Eigenvalue Decomposition-Based Speech Enhancement

d'Olne

Neo

Naylor

2022

2022 International Workshop on Acoustic Signal Enhancement (IWAENC)

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Recent work in speech enhancement has proposed a polynomial eigenvalue decomposition (PEVD) method, yielding significant intelligibility and noise-reduction improvements without introducing distortions in the enhanced signal [1]. The method relies on the estimation of a space-time covariance matrix, performed in batch mode such that a sufficiently long portion of the noisy signal is used to derive an accurate estimate. However, in applications where the scene is nonstationary, this approach is unable to adapt to changes in the acoustic scenario. This paper thus proposes a frame-based procedure for the estimation of space-time covariance matrices and investigates its impact on subsequent PEVD speech enhancement. The method is found to yield spatial filters and speech enhancement improvements comparable to the batch method in [1], showing potential for real-time processing.

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“…where R x k x k (τ ) is the space-time covariance matrix obtained following (5) in the k th frame of length L, such that n ∈ {(k − 1)L, . .…”

Section: Batch Mode Versus Frame-based Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frame-Based Space-Time Covariance Matrix Estimation for Polynomial Eigenvalue Decomposition-Based Speech Enhancement

d'Olne

Neo

Naylor

2022

2022 International Workshop on Acoustic Signal Enhancement (IWAENC)

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“…The whole frequency range is considered in SP-MUSIC [14]. However, in this work, only Ω k in the frequency range of speech (100 Hz to 4000 Hz) [29] are used in (11). A peak detection algorithm [30] is used to estimate the DoAs from (11).…”

Section: Proposed Enhancements For Sound Source Localizationmentioning

confidence: 99%

“…Most of these extensions rely on transforming the broadband DoA problem into several narrowband problems. This can be achieved by decomposing the broadband signal into several independent frequency bins [11]. The resulting narrowband signals for each frequency bin or filtered output can then be processed independently, or incoherently.…”

Section: Introductionmentioning

confidence: 99%

A Polynomial Eigenvalue Decomposition Music Approach for Broadband Sound Source Localization

Hogg

Neo

Weiss³

et al. 2021

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

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Direction of arrival (DoA) estimation for sound source localization is increasingly prevalent in modern devices. In this paper, we explore a polynomial extension to the multiple signal classification (MUSIC) algorithm, spatio-spectral polynomial (SSP)-MUSIC, and evaluate its performance when using speech sound sources. In addition, we also propose three essential enhancements for SSP-MUSIC to work with noisy reverberant audio data. This paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different noise and reverberation conditions and the first task of the LOCATA challenge. We show that SSP-MUSIC is more robust to noise and reverberation compared to independent frequency bin (IFB) approaches and improvements can be seen for single sound source localization at signal-to-noise ratios (SNRs) below 5 dB and reverberation times (T60s) larger than 0.7 s.

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“…The problem of designing a suitable FIR eigenvector matrix U(z) has been the stuff of many research papers, a number of which have considered applications to, for example, subband coder design (Tkacenko 2010;Redif et al 2011), MIMO channel coding (Moret et al 2011), direction of arrival estimation (Alrmah et al 2013), image compression (Carcenac et al 2017) and blind source separation (Redif 2015;Redif et al 2017). The SMD algorithm in and Corr et al (2014) was shown to provide superior convergence speeds when diagonalizing parahermitian matrices.…”

Section: U(z)ũ(z)mentioning

confidence: 99%

Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data

Carcenac¹,

Redif

2019

Comput Stat

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This paper introduces an adaptation of the sequential matrix diagonalization (SMD) method to high-dimensional functional magnetic resonance imaging (fMRI) data. SMD is currently the most efficient statistical method to perform polynomial eigenvalue decomposition. Unfortunately, with current implementations based on dense polynomial matrices, the algorithmic complexity of SMD is intractable and it cannot be applied as such to high-dimensional problems. However, for certain applications, these polynomial matrices are mostly filled with null values and we have consequently developed an efficient implementation of SMD based on a GPU-parallel representation of sparse polynomial matrices. We then apply our "sparse SMD" to fMRI data, i.e. the temporal evolution of a large grid of voxels (as many as 29,328 voxels). Because of the energy compaction property of SMD, practically all the information is concentrated by SMD on the first polynomial principal component. Brain regions that are activated over time are thus reconstructed with great fidelity through analysis-synthesis based on the first principal component only, itself in the form of a sequence of polynomial parameters.

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Angle of Arrival Estimation for Broadband Signals: A Comparison

Cited by 9 publications

References 10 publications

Frame-Based Space-Time Covariance Matrix Estimation for Polynomial Eigenvalue Decomposition-Based Speech Enhancement

Frame-Based Space-Time Covariance Matrix Estimation for Polynomial Eigenvalue Decomposition-Based Speech Enhancement

A Polynomial Eigenvalue Decomposition Music Approach for Broadband Sound Source Localization

Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data

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