Sonification of Spectroscopic analysis of food data using FM Synthesis

Kew H, Stables R. Sonification of Spectroscopic analysis of food data using FM Synthesis. In: AES Convention 149. Audio Engineering Society; 2020. Paper 10394. Available from: https://aes.org/publications/elibrary-page/?id=20931

@inproceedings{Kew2020_20931,
  author = {Kew, Hsein and Stables, Ryan},
  title = {{Sonification of Spectroscopic analysis of food data using FM Synthesis}},
  booktitle = {AES Convention 149},
  note = {Paper 10394},
  year = {2020},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20931}
}

TY  - CPAPER
TI  - Sonification of Spectroscopic analysis of food data using FM Synthesis
AU  - Kew, Hsein
AU  - Stables, Ryan
T2  - AES Convention 149
M1  - Paper 10394
PY  - 2020
DA  - 2020/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20931
PB  - Audio Engineering Society
LA  - en
AB  - Food safety is a global concern, and with the rise of automation, novel methods of categorising, sorting, and discriminating food types are being explored. These techniques require a reliable method for rapidly identifying food sources. In this paper, we propose a method of spectroscopic food analysis, whereby audio data is generated from spectra to allow users to discriminate between two classes of a given food type. To do this, we develop a system which first extracts features and applies dimensionality reduction, then maps them to the parameters of a synthesizer. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. The results indicate that the model is able to provide relevant auditory information, and most importantly, allows users to consistently discriminate between two classes of spectral data. This provides a complementary tool to supplement current food detection methods.
ER  -