@InProceedings{7362791,
  author = {M. H. Bahari and A. Bertrand and M. Moonen},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Blind sampling rate offset estimation based on coherence drift in wireless acoustic sensor networks},
  year = {2015},
  pages = {2281-2285},
  abstract = {In this paper, a new approach for sampling rate offset (SRO) estimation between nodes of a wireless acoustic sensor network (WASN) is proposed using the phase drift of the coherence function between the signals. This method, referred to as least squares coherence drift (LCD) estimation, assumes that the SRO induces a linearly increasing phase-shift in the short-time Fourier transform (STFT) domain. This phase-shift, observed as a drift in the phase of the signal coherence, is applied in a least-squares estimation framework to estimate the SRO. Simulation results in different scenarios show that the LCD estimation approach can estimate the SRO with a mean absolute error of around 1%. We finally demonstrate that the use of the LCD estimation within a compensation approach eliminates the performance-loss due to SRO in a multichannel Wiener filter (MWF)-based speech enhancement task.},
  keywords = {acoustic communication (telecommunication);blind equalisers;Fourier transforms;least squares approximations;speech enhancement;Wiener filters;wireless channels;wireless sensor networks;speech enhancement task;multichannel Wiener filter;STFT domain;short-time Fourier transform domain;least squares coherence drift estimation;phase drift;WASN nodes;blind sampling rate offset estimation;coherence drift;wireless acoustic sensor network;Estimation;Coherence;Delays;Wireless sensor networks;Microphones;Signal processing;Wireless communication;Wireless Acoustic Sensor Networks;Signal Enhancement;Sampling Rate Offset;Coherence Drift},
  doi = {10.1109/EUSIPCO.2015.7362791},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103777.pdf},
}

{"_id":"TbM7E7PJPgHrPfhLw","bibbaseid":"bahari-bertrand-moonen-blindsamplingrateoffsetestimationbasedoncoherencedriftinwirelessacousticsensornetworks-2015","authorIDs":[],"author_short":["Bahari, M. H.","Bertrand, A.","Moonen, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["M.","H."],"propositions":[],"lastnames":["Bahari"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Bertrand"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Moonen"],"suffixes":[]}],"booktitle":"2015 23rd European Signal Processing Conference (EUSIPCO)","title":"Blind sampling rate offset estimation based on coherence drift in wireless acoustic sensor networks","year":"2015","pages":"2281-2285","abstract":"In this paper, a new approach for sampling rate offset (SRO) estimation between nodes of a wireless acoustic sensor network (WASN) is proposed using the phase drift of the coherence function between the signals. This method, referred to as least squares coherence drift (LCD) estimation, assumes that the SRO induces a linearly increasing phase-shift in the short-time Fourier transform (STFT) domain. This phase-shift, observed as a drift in the phase of the signal coherence, is applied in a least-squares estimation framework to estimate the SRO. Simulation results in different scenarios show that the LCD estimation approach can estimate the SRO with a mean absolute error of around 1%. We finally demonstrate that the use of the LCD estimation within a compensation approach eliminates the performance-loss due to SRO in a multichannel Wiener filter (MWF)-based speech enhancement task.","keywords":"acoustic communication (telecommunication);blind equalisers;Fourier transforms;least squares approximations;speech enhancement;Wiener filters;wireless channels;wireless sensor networks;speech enhancement task;multichannel Wiener filter;STFT domain;short-time Fourier transform domain;least squares coherence drift estimation;phase drift;WASN nodes;blind sampling rate offset estimation;coherence drift;wireless acoustic sensor network;Estimation;Coherence;Delays;Wireless sensor networks;Microphones;Signal processing;Wireless communication;Wireless Acoustic Sensor Networks;Signal Enhancement;Sampling Rate Offset;Coherence Drift","doi":"10.1109/EUSIPCO.2015.7362791","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103777.pdf","bibtex":"@InProceedings{7362791,\n author = {M. H. Bahari and A. Bertrand and M. Moonen},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Blind sampling rate offset estimation based on coherence drift in wireless acoustic sensor networks},\n year = {2015},\n pages = {2281-2285},\n abstract = {In this paper, a new approach for sampling rate offset (SRO) estimation between nodes of a wireless acoustic sensor network (WASN) is proposed using the phase drift of the coherence function between the signals. This method, referred to as least squares coherence drift (LCD) estimation, assumes that the SRO induces a linearly increasing phase-shift in the short-time Fourier transform (STFT) domain. This phase-shift, observed as a drift in the phase of the signal coherence, is applied in a least-squares estimation framework to estimate the SRO. Simulation results in different scenarios show that the LCD estimation approach can estimate the SRO with a mean absolute error of around 1%. We finally demonstrate that the use of the LCD estimation within a compensation approach eliminates the performance-loss due to SRO in a multichannel Wiener filter (MWF)-based speech enhancement task.},\n keywords = {acoustic communication (telecommunication);blind equalisers;Fourier transforms;least squares approximations;speech enhancement;Wiener filters;wireless channels;wireless sensor networks;speech enhancement task;multichannel Wiener filter;STFT domain;short-time Fourier transform domain;least squares coherence drift estimation;phase drift;WASN nodes;blind sampling rate offset estimation;coherence drift;wireless acoustic sensor network;Estimation;Coherence;Delays;Wireless sensor networks;Microphones;Signal processing;Wireless communication;Wireless Acoustic Sensor Networks;Signal Enhancement;Sampling Rate Offset;Coherence Drift},\n doi = {10.1109/EUSIPCO.2015.7362791},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103777.pdf},\n}\n\n","author_short":["Bahari, M. H.","Bertrand, A.","Moonen, M."],"key":"7362791","id":"7362791","bibbaseid":"bahari-bertrand-moonen-blindsamplingrateoffsetestimationbasedoncoherencedriftinwirelessacousticsensornetworks-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103777.pdf"},"keyword":["acoustic communication (telecommunication);blind equalisers;Fourier transforms;least squares approximations;speech enhancement;Wiener filters;wireless channels;wireless sensor networks;speech enhancement task;multichannel Wiener filter;STFT domain;short-time Fourier transform domain;least squares coherence drift estimation;phase drift;WASN nodes;blind sampling rate offset estimation;coherence drift;wireless acoustic sensor network;Estimation;Coherence;Delays;Wireless sensor networks;Microphones;Signal processing;Wireless communication;Wireless Acoustic Sensor Networks;Signal Enhancement;Sampling Rate Offset;Coherence Drift"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","creationDate":"2021-02-13T17:31:52.548Z","downloads":0,"keywords":["acoustic communication (telecommunication);blind equalisers;fourier transforms;least squares approximations;speech enhancement;wiener filters;wireless channels;wireless sensor networks;speech enhancement task;multichannel wiener filter;stft domain;short-time fourier transform domain;least squares coherence drift estimation;phase drift;wasn nodes;blind sampling rate offset estimation;coherence drift;wireless acoustic sensor network;estimation;coherence;delays;wireless sensor networks;microphones;signal processing;wireless communication;wireless acoustic sensor networks;signal enhancement;sampling rate offset;coherence drift"],"search_terms":["blind","sampling","rate","offset","estimation","based","coherence","drift","wireless","acoustic","sensor","networks","bahari","bertrand","moonen"],"title":"Blind sampling rate offset estimation based on coherence drift in wireless acoustic sensor networks","year":2015,"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT"]}