Soundscape Audio Signal Classification and Segmentation Using Listeners Perception of Background and Foreground Sound

Thorogood M, Fan J, Pasquier P. Soundscape Audio Signal Classification and Segmentation Using Listeners Perception of Background and Foreground Sound. J Audio Eng Soc. 2016;64(7/8):484-492. doi:10.17743/jaes.2016.0021

@article{Thorogood2016_18334,
  author = {Thorogood, Miles and Fan, Jianyu and Pasquier, Philippe},
  title = {{Soundscape Audio Signal Classification and Segmentation Using Listeners Perception of Background and Foreground Sound}},
  journal = {Journal of the Audio Engineering Society},
  volume = {64},
  number = {7/8},
  pages = {484--492},
  year = {2016},
  month = jul,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2016.0021},
  url = {https://doi.org/10.17743/jaes.2016.0021}
}

TY  - JOUR
TI  - Soundscape Audio Signal Classification and Segmentation Using Listeners Perception of Background and Foreground Sound
AU  - Thorogood, Miles
AU  - Fan, Jianyu
AU  - Pasquier, Philippe
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 64
IS  - 7/8
SP  - 484
EP  - 492
PY  - 2016
DA  - 2016/07/06
DO  - 10.17743/jaes.2016.0021
UR  - https://doi.org/10.17743/jaes.2016.0021
PB  - Audio Engineering Society
LA  - en
AB  - A soundscape recording captures the sonic environment at a given location at a given time using one or more fixed or moving microphones. In most cases, the soundscape is uncontrolled and unscripted. Human listeners experience sonic components as being either background or foreground depending on their salient perceptual characteristics, such as proximity, repetition, and spectral attributes. Analyzing soundscapes in research tasks requires the classification and segmentation of the important sonic components, but that process is time consuming when done manually. This research establishes the background and foreground classification task within a musicological and soundscape context and then presents a method for the automatic segmentation of soundscape recordings. Using a soundscape corpus with ground truth data obtained from a human perception study, the analysis shows that participants have a high level of agreement on the category assigned to background samples (92.5%), foreground samples (80.8%), and background with foreground samples (75.3%). Experiments demonstrate how smaller window sizes affect the performance of the classifier.
ER  -