Acoustic Reflectors Localization from Stereo Recordings Using Neural Networks

Giovanni Bologni, Richard Heusdens, Jorge Martinez

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Acoustic room geometry estimation is often performed in ad hoc settings, i.e., using multiple microphones and sources distributed around the room, or assuming control over the excitation signals. We propose a fully convolutional network (FCN) that localizes reflective surfaces under the relaxed assumptions that (i) a compact array of only two microphones is available, (ii) emitter and receivers are not synchronized, and (iii) both the excitation signals and the impulse responses of the enclosures are unknown. Our FCN is trained in a supervised fashion to predict the likelihood of reflective surfaces at specific distances and directions-of-arrival (DOA). When a single reflective surface is present, up to 80% of real and virtual sources are detected, while this figure approaches 50% in rectangular rooms. Experiments on real-world recordings report similar accuracy as with artificially reverberated speech signals, validating the generalization capabilities of the framework.
Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Place of PublicationPiscataway
Number of pages5
ISBN (Electronic)978-1-7281-7605-5
ISBN (Print)978-1-7281-7606-2
Publication statusPublished - 2021
EventICASSP 2021: The IEEE International Conference on Acoustics, Speech, and Signal Processing - Virtual Conference/Toronto, Canada
Duration: 6 Jun 202111 Jun 2021


ConferenceICASSP 2021
CityVirtual Conference/Toronto


  • Source localization
  • acoustic reflectors
  • walls
  • image-source model
  • convolutional neural network
  • deep learning


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