Discrete time techniques for time delay estimation

This paper addresses the problem of localizing multiple competing speakers in the presence of room reverberation, where sound sources can be positioned at any azimuth on the horizontal plane. To reduce the amount of front-back confusions which can occur due to the similarity of interaural time differences (ITDs) and interaural level differences (ILDs) in the front and rear hemifield, a machine hearing system is presented which combines supervised learning of binaural cues using multi-conditional training (MCT) with a head movement strategy. A systematic evaluation showed that this approach substantially reduced the amount of front-back confusions in challenging acoustic scenarios. Moreover, the system was able to generalize to a variety of different acoustic conditions not seen during training.

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“…To increase the resolution of the final azimuth estimates, parabolic interpolation was applied to refine the peak positions [12].…”

Section: Gmm-based Localizationmentioning

confidence: 99%

Robust localisation of multiple speakers exploiting head movements and multi-conditional training of binaural cues

May

Brown

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In turn, in the case of tracking or localizing a number of sources, the maximum likelihood-based methods are exploited (Ziskind and Wax, 1988) representing the statistical approach. More details can be found in the abundant literature on the localization of acoustic sources for multimedia applications (Jacovitti and Scarano, 1993;Khalil et al, 1994;Mahieux et al, 1996;Wang and Chu, 1997;Zhang and Er, 1996).…”

Section: Experimental Backgroundmentioning

confidence: 99%

Automatic identification of sound source position employing neural networks and rough sets

Czyżewski

2003

Pattern Recognition Letters

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Methods for the identification of direction of the incoming acoustical signal in the presence of noise and reverberation are investigated. Since the problem is a non-deterministic one, thus applications of two learning algorithms, namely neural networks and rough sets are developed to solve it. Consequently, two sets of parameters have been formulated in order to discern target source from unwanted sound source position and then processed by learning algorithms. The applied feature extraction methods are discussed, training processes are described and obtained sound source localizing results are demonstrated and compared.

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“…There are several different ways to implement TDE, 33 and multiple codes are currently used. 17,22,34,35 We will focus on a modified TDE method that has been described in detail before, 16,19 and is summarized here.…”

Section: B Tracking Time-delay Estimationmentioning

confidence: 99%

Comparison of velocimetry techniques for turbulent structures in gas-puff imaging data

Sierchio

Cziegler²,

Terry

et al. 2016

Review of Scientific Instruments

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Recent analysis of Gas Puff Imaging (GPI) data from Alcator C-Mod found blob velocities with a modified tracking time delay estimation (TDE). These results disagree with velocity analysis performed using direct Fourier methods. In this paper, the two analysis methods are compared. The implementations of these methods are explained, and direct comparisons using the same GPI data sets are presented to highlight the discrepancies in measured velocities. In order to understand the discrepancies, we present a code that generates synthetic sequences of images that mimic features of the experimental GPI images, with user-specified input values for structure (blob) size and velocity. This allows quantitative comparison of the TDE and Fourier analysis methods, which reveals their strengths and weaknesses. We found that the methods agree for structures of any size as long as all structures move at the same velocity and disagree when there is significant nonlinear dispersion or when structures appear to move in opposite directions. Direct Fourier methods used to extract poloidal velocities give incorrect results when there is a significant radial velocity component and are subject to the barber pole effect. Tracking TDE techniques give incorrect velocity measurements when there are features moving at significantly different speeds or in different directions within the same field of view. Finally, we discuss the limitations and appropriate use of each of methods and applications to the relationship between blob size and velocity. C 2016 AIP Publishing LLC. [http://dx

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Discrete time techniques for time delay estimation

Cited by 367 publications

References 10 publications

Robust localisation of multiple speakers exploiting head movements and multi-conditional training of binaural cues

Robust localisation of multiple speakers exploiting head movements and multi-conditional training of binaural cues

Automatic identification of sound source position employing neural networks and rough sets

Comparison of velocimetry techniques for turbulent structures in gas-puff imaging data

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