Sonic Data Workshop

Date

Thursday 16 – Friday 17 February 2023, 10am – 6pm, in MMT @ CMC.

Participants

  • YANG Cheng
  • Ningze ‘Nada’ HAN
  • YUE ‘Oksana’ Ran
  • Mirjana DOKIC
  • Gui ‘Gooey’ REN

Organisers

  • PerMagnus ‘PM’ Lindborg, PhD, associate professor
  • Manni ‘$$$’ Chen, doctoral candidate

Content

To explore computer tools for music information retrieval (MIR), sonic information retrieval (SIR), descriptive analysis, modelling, and plotting.

Objectives

Participants will be able to identify principles of computer analysis of sound recordings, such as feature extraction, statistical descriptors, factor analysis, and regression modelling, as well as data visualisation.

The workshop has three main objectives:

  1. to familiarise participants with working in R, for general-purpose analysis and visualisation (data plotting);
  2. to get knowledge about MIR and SIR, using R;
  3. to get insights into sonic data analysis in practice, via examples from published studies.

Schedule

Before workshop starts

  1. install R (“R studio”) on your own computer, and familiarise yourself with it; links below
  2. read Kabacoff ch. 1 carefully and follow each of the given demos; (PDF distributed to participants)
  3. look into Kabacoff ch. 2 to get an overview of R data handling and grammar; (PDF distributed to participants)
  4. look into Wickham ch. 1+2 for an overview of ggplot2;  (PDF distributed to participants)

Thursday

  1. Using R – overview and examples (Kabacoff) (PM) – CityU online book
  2. Music Information Retrieval [MIR] in R (MC)
    • Example:: logistic regression (Chen, Lindborg 2022) (MC)
  3. Visualisation in R – base and ggplot2 (PM) – CityU library download
  4. Plotting and lm, play around with some test data

Friday

  1. Data elicitation and analysis – overview and QuestionPro examples (PM)
    • Example of QPro –> R (Lindborg, Liew 2022) (PM)
  2. exploratory analysis – fa, linreg, princomp, MST, correlations:
    • factor analysis, example from (Lindborg, Lenzi, Chen 2023) (PM)
  3. soundscape information retrieval
    • bioacoustics, example from (Lenzi, Lindborg, Sabada 2021) (PM)
  4. Exercises
    • library: base
    • library: bioacoustics

Resources

Workshop materials

Slides (PDF) and Examples :: Google folder (viewable)

Software

Books

  • Kabacoff, R. (2022). R in Action: Data Analysis and Graphics with R and Tidyverse: Simon and Schuster – CityU ebook
  • Wickham, H., Chang, W., & Wickham, M. H. (2016). Package ‘ggplot2’. Create Elegant Data Visualisations Using the Grammar of Graphics. Version, 2(1), 1-189 – CityU ebook
  • Springer Book Series :: Use R!

Packages

MIR/SIR

environmental acoustics, bioacoustics

generation

Analysis

  • base (summary, data.frame, etc…)
  • fa
  • princomp
  • lm
  • …

Visualisation

  • base (boxplot, hist…)
  • ggplot2

References

Anikin, A. (2017). Package ‘soundgen’. In.
Bailes, F., & Dean, R. T. (2012). Comparative time series analysis of perceptual responses to electroacoustic music. Music Perception: An Interdisciplinary Journal, 29(4), 359-375.
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., . . . Scheipl, F. (2012). Package ‘lme4’. CRAN. R Foundation for Statistical Computing, Vienna, Austria.
Chen, M., & Lindborg, P. (2023/01). Observations on Guitar Music Produced by AI Reverberation and Professional Sound Engineer. International Journal of Music Science, Technology and Art (IJMSTA), Vol. 5(1) – January 2023.
Chollet, F. J. Allaire J. 2018. Deep learning with R. In: Manning Publications, Shelter Island, NY.
Dean, R. T., & Bailes, F. (2010, Oct.). Time series analysis as a method to examine acoustical influences on real-time perception of music. Empirical Musicology Review, 5:4, 152-175.
Galili, T. (2016, 2016-01-27). Intro to Sound Analysis with R. Retrieved from https://www.r-bloggers.com/2016/01/intro-to-sound-analysis-with-r/
Hermann, T., Hunt, A., & Neuhoff, J. G. (2011). The sonification handbook: Logos Verlag Berlin.
Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’Hara-Wild, M., . . . Yasmeen, F. (2018). forecast: Forecasting functions for time series and linear models. R package version 8.4. URL: https://CRAN. R-project. org/package= forecast.
Kabacoff, R. (2022). R in Action: Data Analysis and Graphics with R and Tidyverse: Simon and Schuster.
Lenzi, S., Sádaba, J., & Lindborg, P. (2021). Soundscape in Times of Change: Case Study of a City Neighbourhood During the COVID-19 Lockdown. Frontiers in psychology, 12(412). doi:10.3389/fpsyg.2021.570741
Liew, K., & Lindborg, P. (2020). A Sonification of Cross-Cultural Differences in Happiness-Related Tweets. Journal of the Audio Engineering Society, 68(1/2), 25-33.
Lindborg, P. (2015a). Psychoacoustic, physical, and perceptual features of restaurants: A field survey in Singapore. Applied acoustics, 92, 47-60.
Lindborg, P. (2016). A taxonomy of sound sources in restaurants. Applied acoustics, 110, 297-310.
Lindborg, P., & Friberg, A. (2016). Personality traits bias the perceived quality of sonic environments. Applied Sciences, 6(12), 405.
Lindborg, P., & Kwan, N. A. (2015). Audio Quality Moderates Localization Accuracy: Two Distinct Perceptual Effects? Paper presented at the Audio Engineering Society Convention 138.
Lindborg, P., Lenzi, S., & Chen, M. (2022). Climate Data Sonification and Visualisation: An Analysis of Topics, Aesthetics, and Characteristics in 32 Recent Projects. Frontiers in psychology, 13, 8663.
Liu, C. J. (2016). beautiful graphics ggplot2. Retrieved from https://rstudio-pubs-static.s3.amazonaws.com/228019_f0c39e05758a4a51b435b19dbd321c23.html
Liu, V. (2022, 2022-12-26). A Comprehensive Guide to Graph Customization with R GGplot2 Package. Retrieved from https://towardsai.net/p/l/a-comprehensive-guide-to-graph-customization-with-r-ggplot2-package
Meik Michalke, Earl Brown, Alberto Mirisola, Alexandre Brulet, & Hauser, L. (2021). koRpus v0.13-8 (package for R). In.
R Core Team. (2022). R: A language and environment for statistical computing. Version 4.05. In: R Foundation for Statistical Computing.
Sueur, J. (2018). Sound analysis and synthesis with R: Springer.
Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave, a free modular tool for sound analysis and synthesis. Bioacoustics, 18(2), 213-226.
Sueur, J., Aubin, T., Simonis, C., Lellouch, L., Brown, E. C., Depraetere, M., . . . Kasten, E. (2019). Package ‘seewave’.
Sueur, J., Farina, A., Gasc, A., Pieretti, N., & Pavoine, S. (2014). Acoustic indices for biodiversity assessment and landscape investigation. Acta Acustica united with Acustica, 100(4), 772-781.
Sueur, J., Pavoine, S., Hamerlynck, O., & Duvail, S. (2008). Rapid acoustic survey for biodiversity appraisal. PloS one, 3(12). Retrieved from https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0004065
Villanueva-Rivera, L. J., Pijanowski, B. C., & Villanueva-Rivera, M. L. J. (2018). Package ‘soundecology’.
Wickham, H., Chang, W., & Wickham, M. H. (2016). Package ‘ggplot2’. Create Elegant Data Visualisations Using the Grammar of Graphics. Version, 2(1), 1-189.

Websites

Sound analysis in R :: https://www.r-bloggers.com/2016/01/intro-to-sound-analysis-with-r/

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