Joint DOA and multi-pitch estimation based on subspace techniques

Zhang, Johan Xi; Christensen, Mads Græsbøll; Jensen, Søren Holdt; Moonen, Marc

doi:10.1186/1687-6180-2012-1

Cited by 207 publications

(36 citation statements)

References 31 publications

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“…Other approaches that are capable of this are [20,21]. The optimal filter for extracting the periodic source with fundamental frequencyω t and DOAθ is then obtained by inserting these quantities into the optimal filter design (4).…”

Section: Optimal Filters For Periodic Sourcesmentioning

confidence: 99%

Spatio-temporal filtering methods for enhancement and separation of speech signals

Christensen

Jensen

Benesty

et al. 2013

2013 IEEE China Summit and International Conference on Signal and Information Processing

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General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access to the work immediately and investigate your claim. ABSTRACTIn this paper, we give an overview of the background for, the ideas behind, and the challenges to be addressed in the project "Spatio-Temporal Filtering Methods for Enhancement and Separation of Speech Signals," which is funded by the Villum Foundation. The project aims at addressing the problem of enhancing and separating speech signals from noisy mixtures, a problem also known as the cocktail party problem. It aims at exploring new ways of solving this problem by generalizing a new class of optimal temporal filtering methods for periodic signals to multiple microphones, resulting in socalled spatio-temporal filtering methods that are controlled by two parameters, the direction-of-arrival and the fundamental frequency. These filters are optimal in that they let the signal of interest pass undistorted while everything else is attenuated as much as possible. Unlike state-of-the-art methods, they do not require knowledge of the statistics of noise and interfering speech signals, something that is especially important when dealing with non-stationary noise.

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Section: Optimal Filters For Periodic Sourcesmentioning

confidence: 99%

Spatio-temporal filtering methods for enhancement and separation of speech signals

Christensen

Jensen

Benesty

et al. 2013

2013 IEEE China Summit and International Conference on Signal and Information Processing

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“…Much of the work within the area has focused on reliable estimation of signals containing only a single fundamental frequency, or pitch, such as [2], although there has, during recent years, also been efforts on estimating the fundamental frequencies of signals containing multiple pitches, see e.g., [3][4][5]. Notable contributions on exploiting or extracting spatial information of sound sources have also been developed, wherein an array of receiver sensors are used to separate the sources (see, e.g., [6][7][8]). However, typically existing methods for joint pitch and directionsof-arrival (DOA) estimation assumes strong a priori knowledge of the sound signals, such as knowledge of the number of sources, and the number of harmonics present in each pitch signal, and are also often restricted to only allow for single pitch sound sources.…”

Section: Introductionmentioning

confidence: 99%

Joint DOA and multi-pitch estimation using block sparsity

Kronvall

Adalbjörnsson

Jakobsson

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a novel method to estimate the fundamental frequencies and directions-of-arrival (DOA) of multi-pitch signals impinging on a sensor array. Formulating the estimation as a group sparse convex optimization problem, we use the alternating direction of multipliers method (ADMM) to estimate both temporal and spatial correlation of the array signal. By first jointly estimating both fundamental frequencies and time-of-arrivals (TOAs) for each sensor and sound source, we then form a non-linear least squares estimate to obtain the DOAs. Numerical simulations indicate the preferable performance of the proposed estimator as compared to current state-of-the-art methods.

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“…It should also be noted that the DOA along with the pitch also are believed to be some of the governing factors that the human auditory system uses for separating sources. This line of reasoning has, quite recently, led to some joint DOA and fundamental frequency estimators, including maximum likelihood based [10], [11], subspace-based [12]- [14], correlation-based [15], [16], and filtering-based [17]- [19] methods. Notably, the problem of joint DOA and fundamental frequency estimation was formalized and thoroughly analyzed in [10], and a maximum likelihood estimator that achieves the highest possible accuracy (under certain conditions) was proposed.…”

Section: Introductionmentioning

confidence: 99%

Joint Spatio-Temporal Filtering Methods for DOA and Fundamental Frequency Estimation

Jensen

Christensen

Benesty

et al. 2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-In this paper, spatio-temporal filtering methods are proposed for estimating the direction-of-arrival (DOA) and fundamental frequency of periodic signals, like those produced by the speech production system and many musical instruments using microphone arrays. This topic has quite recently received some attention in the community and is quite promising for several applications. The proposed methods are based on optimal, adaptive filters that leave the desired signal, having a certain DOA and fundamental frequency, undistorted and suppress everything else. The filtering methods simultaneously operate in space and time, whereby it is possible resolve cases that are otherwise problematic for pitch estimators or DOA estimators based on beamforming. Several special cases and improvements are considered, including a method for estimating the covariance matrix based on the recently proposed iterative adaptive approach (IAA). Experiments demonstrate the improved performance of the proposed methods under adverse conditions compared to the state of the art using both synthetic signals and real signals, as well as illustrate the properties of the methods and the filters.Index Terms-2-D filtering, DOA estimation, fundamental frequency estimation, joint estimation, LCMV beamformer, periodogram-based beamformer.

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Joint DOA and multi-pitch estimation based on subspace techniques

Cited by 207 publications

References 31 publications

Spatio-temporal filtering methods for enhancement and separation of speech signals

Spatio-temporal filtering methods for enhancement and separation of speech signals

Joint DOA and multi-pitch estimation using block sparsity

Joint Spatio-Temporal Filtering Methods for DOA and Fundamental Frequency Estimation

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