A model for spectroscopic food sample analysis using data sonification

Kew, Hsein (2021) A model for spectroscopic food sample analysis using data sonification. International Journal of Speech Technology, 24 (4). pp. 865-881. ISSN 1572-8110

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Abstract

Abstract: In this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. 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. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

Item Type:	Article
Additional Information:	** From Springer Nature via Jisc Publications Router ** History: received 18-07-2020; registration 31-12-2020; accepted 31-12-2020; pub-electronic 13-01-2021; online 13-01-2021; pub-print 12-2021. ** Licence for this article: http://creativecommons.org/licenses/by/4.0/
Identification Number:	10.1007/s10772-020-09794-9
Dates:	Date Event 31 December 2020 Accepted 13 January 2021 Published Online
Uncontrolled Keywords:	Article, Spectroscopic analysis, Machine learning, Dimensionality reduction, Sound synthesiser, Audio processing
Subjects:	CAH02 - subjects allied to medicine > CAH02-05 - medical sciences > CAH02-05-03 - biomedical sciences (non-specific) CAH10 - engineering and technology > CAH10-03 - materials and technology > CAH10-03-05 - biotechnology
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
SWORD Depositor:	JISC PubRouter
Depositing User:	JISC PubRouter
Date Deposited:	30 Nov 2021 11:24
Last Modified:	22 Mar 2023 12:00
URI:	https://www.open-access.bcu.ac.uk/id/eprint/12435

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