A Sonification Algorithm for Subjective Classification of Food Samples

Kew H, Stables R. A Sonification Algorithm for Subjective Classification of Food Samples. In: AES Convention 149. Audio Engineering Society; 2020. Paper 10393. Available from: https://aes.org/publications/elibrary-page/?id=20930

@inproceedings{Kew2020_20930,
  author = {Kew, Hsein and Stables, Ryan},
  title = {{A Sonification Algorithm for Subjective Classification of Food Samples}},
  booktitle = {AES Convention 149},
  note = {Paper 10393},
  year = {2020},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20930}
}

TY  - CPAPER
TI  - A Sonification Algorithm for Subjective Classification of Food Samples
AU  - Kew, Hsein
AU  - Stables, Ryan
T2  - AES Convention 149
M1  - Paper 10393
PY  - 2020
DA  - 2020/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20930
PB  - Audio Engineering Society
LA  - en
AB  - Spectroscopic food analysis has been much studied over many years and is still an ongoing topic of research. In this paper, we propose a spectroscopic diagnostic method to generate an audio output in order to discriminate between two classes of data, based on the features of spectral datasets. To do this, we first perform spectral pre-processing and extract appropriate features from the spectra, and then apply different selection criteria to narrow down the number of features selected. To optimise the process, we compare three selection criteria, as applied to two spectroscopy food datasets in order to evaluate the performance of sonification as a method for discriminating data. Lastly, the salient features are mapped to the parameters of a frequency modulation (FM) synthesizer, so as to generate audio samples. The results indicate that the model is able to provide relevant auditory information, and most importantly, allows users to discriminate consistently between two classes of spectral data. This approach using the sonification of spectroscopic data has been shown to be useful to a food analyst as a new method of investigating the purity of food.
ER  -