Acoustic-Beamforming-Advanced

MATLAB code for the baseline of "Learning an Interpretable End-to-End Network for Real-Time Acoustic Beamforming".

If you use the code, please cite our paper:

Liang, Hao and Zhou, Guanxing and Tu, Xiaotong and Jakobsson, Andreas and Ding, Xinghao and Huang, Yue, "Learning an Interpretable End-to-End Network for Real-Time Acoustic Beamforming", Journal of Sound and Vibration, 2024.

Also, the main code for "Learning an Interpretable End-to-End Network for Real-Time Acoustic Beamforming" is available at https://github.com/JoaquinChou/DAMAS_FISTA_Net or https://github.com/HauLiang/DAMAS-FISTA-Net (latest version).

Preparation

• MATLAB >= R2022a

Getting Started

This scan-frequency demo for acoustic imaging mainly contains the model-based acoustic beamforming methods.

The previous version can be found at https://github.com/HauLiang/Acoustic-Beamforming-Methods.

As for the model-based methods:

• DAS:
  

  Van Veen, Barry D and Buckley, Kevin M, "Beamforming: A versatile approach to spatial filtering", IEEE assp magazine, 1988.

• MUSIC:
  

  Schmidt, Ralph, "Multiple emitter location and signal parameter estimation", IEEE transactions on antennas and propagation, 1986.

• DAMAS:
  

  Brooks, Thomas F and Humphreys, William M, "A deconvolution approach for the mapping of acoustic sources (DAMAS) determined from phased microphone arrays", Journal of sound and vibration, 2006.

• DAMAS2:
  

  Dougherty, Robert, "Extensions of DAMAS and benefits and limitations of deconvolution in beamforming", 11th AIAA/CEAS aeroacoustics conference, 2005.

• DAMAS-FISTA:
  

  Liang, Hao and Zhou, Guanxing and Tu, Xiaotong and Jakobsson, Andreas and Ding, Xinghao and Huang, Yue, "Learning an Interpretable End-to-End Network for Real-Time Acoustic Beamforming", The Journal of Sound and Vibration, 2024.

• CLEAN-PSF:
  

  Högbom, JA, "Aperture synthesis with a non-regular distribution of interferometer baselines", Astronomy and Astrophysics Supplement Series, 1974.

• CLEAN-SC:
  

  Sijtsma, Pieter, "CLEAN based on spatial source coherence", International journal of aeroacoustics, 2007.

• FFT-NNLS:
  

  Ehrenfried, Klaus and Koop, Lars, "Comparison of iterative deconvolution algorithms for the mapping of acoustic sources", AIAA journal, 2007.

• FFT-FISTA are available at https://github.com/dtu-act/EMBED:
  

  Lylloff, Oliver and Fernández-Grande, Efrén and Agerkvist, Finn and Hald, Jørgen and Tiana Roig, Elisabet and Andersen, Martin S. "Improving the efficiency of deconvolution algorithms for sound source localization". The journal of the acoustical society of America, 2015.

• FFT-DFISTA:
  

  Ding, Xinghao and Liang, Hao and Jakobsson, Andreas and Tu, Xiaotong and Huang, Yue. "High-Resolution Source Localization Exploiting the Sparsity of the Beamforming Map", Signal Processing, 2022.

As for the deep network-based method:

• Acoustic-Net are available at https://github.com/JoaquinChou/Acousitc-Net:
  

  Zhou, Guanxing and Liang, Hao and Ding, Xinghao and Huang, Yue and Tu, Xiaotong and Abbas, Saqlain. "Acoustic-Net: A Novel Neural Network for Sound Localization and Quantification", in 19th Asia-Pacific Vibration Conference (APVC2021), 2022.

If you want to know more about acoustic imaging, please refer to the following papers:

• Fundamentals of acoustic beamforming:

  de Santana, Leandro, "Fundamentals of Acoustic Beamforming", Design and Operation of Aeroacoustic Wind Tunnel Tests for Group and Air Transport, 2017.

• A review of acoustic imaging methods:

  Merino-Martínez, Roberto and Sijtsma, Pieter and Snellen, Mirjam and Ahlefeldt, Thomas and Antoni, Jerome and Bahr, Christopher J and Blacodon, Daniel and Ernst, Daniel and Finez, Arthur and Funke, Stefan and others, "A review of acoustic imaging methods using phased microphone arrays", CEAS Aeronautical Journal, 2019.

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