TR2017-013

Jazz: A Companion to Music for Frequency Estimation with Missing Data


    •  Li, Q., Liu, S., Mansour, H., Wakin, M., Yang, D., Zhu, Z., "Jazz: A Companion to Music for Frequency Estimation with Missing Data", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017.
      BibTeX TR2017-013 PDF
      • @inproceedings{Li2017mar,
      • author = {Li, Qiuwei and Liu, Shuang and Mansour, Hassan and Wakin, Michael and Yang, Dehui and Zhu, Zhihui},
      • title = {Jazz: A Companion to Music for Frequency Estimation with Missing Data},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2017,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2017-013}
      • }
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  • Research Area:

    Computational Sensing

Abstract:

Frequency estimation is a classical problem in signal processing, with applications ranging from sensor array processing to wireless communications and structural health monitoring. Modern algorithms based on atomic norm minimization can cope with missing data but incur a high computational cost. To recover missing data from an ensemble of frequency-sparse signals, we propose a computationally efficient low-rank tensor completion algorithm that exploits the fact that each signal in the ensemble can be associated with a Toeplitz matrix. We name our algorithm JAZZ in the spirit of the classical MUSIC algorithm for frequency estimation and in tribute to the random, improvisational nature of jazz music.

 

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      Date: March 5, 2017 - March 9, 2017
      Where: New Orleans
      MERL Contacts: Petros T. Boufounos; Takaaki Hori; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Anthony Vetro; Ye Wang
      Research Areas: Computer Vision, Computational Sensing, Digital Video, Information Security, Speech & Audio
      Brief
      • MERL researchers will presented 10 papers at the upcoming IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), to be held in New Orleans from March 5-9, 2017. Topics to be presented include recent advances in speech recognition and audio processing; graph signal processing; computational imaging; and privacy-preserving data analysis.

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