Near-field localization of audio: A maximum likelihood approach. Jensen, J. R. & Christensen, M. G. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 895-899, Sep., 2014.
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Paper abstract bibtex
Paper abstract bibtex Localization of audio sources using microphone arrays has been an important research problem for more than two decades. Many traditional methods for solving the problem are based on a two-stage procedure: first, information about the audio source, such as time differences-of-arrival (TDOAs) and gain ratios-of-arrival (GROAs) between microphones is estimated, and, second, this knowledge is used to localize the audio source. These methods often have a low computational complexity, but this comes at the cost of a limited estimation accuracy. Therefore, we propose a new localization approach, where the desired signal is modeled using TDOAs and GROAs, which are determined by the source location. This facilitates the derivation of one-stage, maximum likelihood methods under a white Gaussian noise assumption that is applicable in both near- and far-field scenarios. Simulations show that the proposed method is statistically efficient and outperforms state-of-the-art estimators in most scenarios, involving both synthetic and real data.
@InProceedings{6952298,
author = {J. R. Jensen and M. G. Christensen},
booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
title = {Near-field localization of audio: A maximum likelihood approach},
year = {2014},
pages = {895-899},
abstract = {Localization of audio sources using microphone arrays has been an important research problem for more than two decades. Many traditional methods for solving the problem are based on a two-stage procedure: first, information about the audio source, such as time differences-of-arrival (TDOAs) and gain ratios-of-arrival (GROAs) between microphones is estimated, and, second, this knowledge is used to localize the audio source. These methods often have a low computational complexity, but this comes at the cost of a limited estimation accuracy. Therefore, we propose a new localization approach, where the desired signal is modeled using TDOAs and GROAs, which are determined by the source location. This facilitates the derivation of one-stage, maximum likelihood methods under a white Gaussian noise assumption that is applicable in both near- and far-field scenarios. Simulations show that the proposed method is statistically efficient and outperforms state-of-the-art estimators in most scenarios, involving both synthetic and real data.},
keywords = {audio signal processing;computational complexity;Gaussian noise;maximum likelihood estimation;microphone arrays;time-of-arrival estimation;state-of-the-art estimators;white Gaussian noise;computational complexity;gain ratios-of-arrival;time differences-of-arrival;microphone arrays;audio sources localization;maximum likelihood;audio near-field localization;Microphones;Direction-of-arrival estimation;Noise;Speech;Harmonic analysis;Maximum likelihood estimation;Audio localization;microphone array;maximum likelihood;near-field;time difference-of-arrival;gain ratio-of-arrival},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569921259.pdf},
}Downloads: 0
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