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Classifying watermelon ripeness by analysing acoustic signals using mobile devices

Zeng, Wei, Huang, Xianfeng, Müller Arisona, Stefan, McLoughlin, Ian Vince (2014) Classifying watermelon ripeness by analysing acoustic signals using mobile devices. Personal and Ubiquitous Computing, 18 (7). pp. 1753-1762. ISSN 1617-4909. E-ISSN 1617-4917. (doi:10.1007/s00779-013-0706-7) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s00779-013-0706-7

Abstract

This work addresses the problem of distinguishing between ripe and unripe watermelons using mobile devices. Through analysing ripeness-related features extracted by thumping watermelons, collecting acoustic signals by microphones on mobile devices, our method can automatically identify the ripeness of watermelons. This is possible in real time, making use of machine learning techniques to provide good accuracy. We firstly collect a training dataset comprising acoustic signals generated by thumping both ripe and unripe watermelons. Audio signal analysis on this helps identify features related to watermelon ripeness. These features are then used to construct a classification model for future signals. Based on this, we developed a crowdsourcing application for Android which allows users to identify watermelon ripeness in real time while submitting their results to us allowing continuous improvement of the classification model. Experimental results show that our method is currently able to correctly classify ripe and unripe watermelons with an overall accuracy exceeding 89 %.

Item Type: Article
DOI/Identification number: 10.1007/s00779-013-0706-7
Uncontrolled keywords: Industry Sectors Automotive Electronics IT & Software Telecommunications Consumer Packaged Goods Aerospace Engineering Authors Wei Zeng zeng@arch.ethz.ch (1) Xianfeng Huang (1) Stefan Müller Arisona (1) Ian Vince McLoughlin (2) Author Affiliations 1. Future Cities Laboratory, Department of Architecture, ETH Zurich, 8093, Zurich, Switzerland 2. University of Science and Technology of China, Hefei, China
Subjects: T Technology
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 25 Aug 2015 09:48 UTC
Last Modified: 29 May 2019 14:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48926 (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince: https://orcid.org/0000-0001-7111-2008
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