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Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12349))

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Abstract

Humans can robustly recognize and localize objects by integrating visual and auditory cues. While machines are able to do the same now with images, less work has been done with sounds. This work develops an approach for dense semantic labelling of sound-making objects, purely based on binaural sounds. We propose a novel sensor setup and record a new audio-visual dataset of street scenes with eight professional binaural microphones and a 360\(^{\circ }\) camera. The co-existence of visual and audio cues is leveraged for supervision transfer. In particular, we employ a cross-modal distillation framework that consists of a vision ‘teacher’ method and a sound ‘student’ method – the student method is trained to generate the same results as the teacher method. This way, the auditory system can be trained without using human annotations. We also propose two auxiliary tasks namely, a) a novel task on Spatial Sound Super-resolution to increase the spatial resolution of sounds, and b) dense depth prediction of the scene. We then formulate the three tasks into one end-to-end trainable multi-tasking network aiming to boost the overall performance. Experimental results on the dataset show that 1) our method achieves good results for all the three tasks; and 2) the three tasks are mutually beneficial – training them together achieves the best performance and 3) the number and the orientations of microphones are both important. The data and code will be released on the project page.

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Acknowledgement

This work is funded by Toyota Motor Europe via the research project TRACE-Zurich. We would like to thank Danda Pani Paudel, Suryansh Kumar and Vaishakh Patil for helpful discussions.

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Authors and Affiliations

  1. CVL, ETH Zurich, Zurich, Switzerland

    Arun Balajee Vasudevan, Dengxin Dai & Luc Van Gool

  2. PSI, KU Leuven, Leuven, Belgium

    Luc Van Gool

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  1. Arun Balajee Vasudevan
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  2. Dengxin Dai
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  3. Luc Van Gool
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Correspondence to Arun Balajee Vasudevan .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Vasudevan, A.B., Dai, D., Van Gool, L. (2020). Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12349. Springer, Cham. https://doi.org/10.1007/978-3-030-58548-8_37

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  • DOI: https://doi.org/10.1007/978-3-030-58548-8_37

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