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Definition-independent Formalization of Soundscapes: Towards a Formal Methodology (2310.13404v1)

Published 20 Oct 2023 in cs.SD, cs.CV, and eess.AS

Abstract: Soundscapes have been studied by researchers from various disciplines, each with different perspectives, goals, approaches, and terminologies. Accordingly, depending on the field, the concept of a soundscape's components changes, consequently changing the basic definition. This results in complicating interdisciplinary communication and comparison of results. Especially when soundscape-unrelated research areas are involved. For this reason, we present a potential formalization that is independent of the underlying soundscape definition, with the goal of being able to capture the heterogeneous structure of the data as well as the different ideologies in one model. In an exemplary analysis of frequency correlation matrices for land use type detection as an alternative to features like MFCCs, we show a practical application of our presented formalization.

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References (51)
  1. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. The Journal of Neuroscience : the official Journal of the Society for Neuroscience, 26:63–72, Jan 2006.
  2. Urban sound classification: Striving towards a fair comparison. arXiv preprint arXiv:2010.11805, 2020.
  3. Changes in noise levels in the city of madrid during covid-19 lockdown in 2020. The Journal of the Acoustical Society of America, 2020.
  4. Acoustic scene classification: Classifying environments from the sounds they produce. IEEE Signal Processing Magazine, 32(3):16–34, 2015.
  5. Investigating changes in noise pollution due to the covid-19 lockdown: The case of dublin, ireland. Sustainable Cities and Society, 2021.
  6. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828, 2013. doi: 10.1109/TPAMI.2013.50.
  7. Ebk-means: A clustering technique based on elbow method and k-means in wsn. International Journal of Computer Applications, 105(9), 2014.
  8. Optimizing the latent space of generative networks. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 600–609. PMLR, 10–15 Jul 2018. URL https://proceedings.mlr.press/v80/bojanowski18a.html.
  9. Machine learning algorithms for environmental sound recognition: Towards soundscape semantics. In Proceedings of the Audio Mostly 2015 on Interaction With Sound, pages 1–7. 2015.
  10. Generating sentences from a continuous space. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, pages 10–21, Berlin, Germany, August 2016. Association for Computational Linguistics. doi: 10.18653/v1/K16-1002. URL https://aclanthology.org/K16-1002.
  11. Urban sound classification using convolutional neural network and long short term memory based on multiple features. In 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), pages 1–9. IEEE, 2020.
  12. The soundscape approach for early stage urban planning: a case study. In Noise Control Engineering, 39th International congress, Proceedings, page 10, 2010.
  13. DIN ISO 12913-1:2018-02. Acoustics - soundscape - part 1: Definition and conceptual framework (iso 12913-1:2014), 2018.
  14. Influence of urban climate on perception responses in soundwalks: case study aachen. In Proceedings of 9th International Conference on Urban Climate jointly with 12th Symposium on Urban Environment–ICUC9, pages 20–24, 2015.
  15. Almo Farina. Soundscape ecology: Principles, patterns, methods and applications. Springer, 2014.
  16. Patterns and dynamics of (bird) soundscapes: A biosemiotic interpretation. Semiotica, 2014(198), 2014.
  17. Ross Girshick. Fast r-cnn. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440–1448, 2015. doi: 10.1109/ICCV.2015.169.
  18. Vae-sne: a deep generative model for simultaneous dimensionality reduction and clustering. BioRxiv, pages 2020–07, 2020.
  19. What do we mean by “soundscape”? a functional description. Frontiers in Ecology and Evolution, page 563, 2022.
  20. The acoustic quality and health in urban environments (salve) project: Study design, rationale and methodology. Applied Acoustics, 2022a. 35.
  21. Analysing interlinked frequency dynamics of the urban acoustic environment. International Journal of Environmental Research and Public Health, 19(22), 2022b. ISSN 1660-4601. doi: 10.3390/ijerph192215014.
  22. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1026–1034, 2015. doi: 10.1109/ICCV.2015.123.
  23. beta-VAE: Learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations, 2017. URL https://openreview.net/forum?id=Sy2fzU9gl.
  24. Impact of the covid-19 lockdown measures on noise levels in urban areas—a pre/during comparison of long-term sound pressure measurements in the ruhr area, germany. International Journal of Environmental Research and Public Health, 2021.
  25. International Organization for Standardization. Iso 12913-1: 2014 acoustics—soundscape—part 1: definition and conceptual framework. ISO, Geneva, 2014.
  26. Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5967–5976, 2017. doi: 10.1109/CVPR.2017.632.
  27. Speech recognition using mfcc. In International Conference on Computer Graphics, Simulation and Modeling, volume 9, 2012.
  28. Classification of urban park soundscapes through perceptions of the acoustical environments. Landscape and Urban Planning, 141:100–111, 2015.
  29. The impact and outreach of soundscape research. Environments, 5(5):58, 2018.
  30. Schulte-Fortkamp B. Kang, J. Soundscape and the Built Environment. CRC Press, 2016. doi: https://doi.org/10.1201/b19145. 1st ed.
  31. Auto-encoding variational bayes. In Yoshua Bengio and Yann LeCun, editors, 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014. URL http://arxiv.org/abs/1312.6114.
  32. Bernie Krause. Anatomy of the soundscape: evolving perspectives. Journal of the Audio Engineering Society, 56(1/2):73–80, 2008.
  33. S. Kullback and R. A. Leibler. On Information and Sufficiency. The Annals of Mathematical Statistics, 22(1):79 – 86, 1951. doi: 10.1214/aoms/1177729694. URL https://doi.org/10.1214/aoms/1177729694.
  34. Urban sound classification using long short-term memory neural network. In 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 57–60. IEEE, 2019.
  35. Urban sound classification using cnn. In 2021 6th International Conference on Inventive Computation Technologies (ICICT), pages 583–589. IEEE, 2021.
  36. Use of noise correlation matrices to interpret ocean ambient noise. The Journal of the Acoustical Society of America, 145(4):2337–2349, 2019.
  37. Reconstruction of gaussian and log-normal fields with spectral smoothness. Physical Review E, 87(3):032136, 2013.
  38. Context encoders: Feature learning by inpainting. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2536–2544, 2016. doi: 10.1109/CVPR.2016.278.
  39. What is soundscape ecology? an introduction and overview of an emerging new science. Landscape Ecology, 26(9):1213–1232, 2011.
  40. Regionalverband Ruhr. Flächennutzungskartierung. Daten für die Stadt- und Regionalplanung. https://www.rvr.ruhr/daten-digitales/geodaten/flaechennutzungskartierung/, 2020. Accessed on 2022-02-02.
  41. Unsupervised feature learning for urban sound classification. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 171–175. IEEE, 2015.
  42. R Murray Schafer. The tuning of the world: Toward a theory of soundscape design. 1977.
  43. Michael Frank Southworth. The sonic environment of cities. PhD thesis, Massachusetts Institute of Technology, 1967.
  44. Classification of soundscapes of urban public open spaces. Landscape and Urban Planning, 189:139–155, 2019.
  45. Impress: A machine learning approach to soundscape affect classification for a music performance environment. In NIME, pages 256–260, 2013.
  46. A tool for urban soundscape evaluation applying support vector machines for developing a soundscape classification model. Science of the Total Environment, 482:440–451, 2014.
  47. Patrik Waldmann. On the use of the pearson correlation coefficient for model evaluation in genome-wide prediction. Frontiers in Genetics, 10:899, 2019.
  48. Hildegard Westerkamp. Linking soundscape composition and acoustic ecology. Organised Sound, 7(1):51–56, 2002.
  49. Wildlife Acoustics. Song meter sm4 acoustic recorder. https://www.wildlifeacoustics.com, 2020. Accessed on 2022-02-02.
  50. Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In International Conference on Machine Learning, pages 3861–3870. PMLR, 2017.
  51. Research on k-value selection method of k-means clustering algorithm. J, 2(2):226–235, 2019.
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