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Classifying watermelon ripeness by analysing acoustic signals using mobile devices

Zeng, Wei ; Huang, Xianfeng ; Müller Arisona, Stefan ; McLoughlin, Ian

In: Personal and Ubiquitous Computing, 2014, vol. 18, no. 7, p. 1753-1762

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    Title
    • Classifying watermelon ripeness by analysing acoustic signals using mobile devices
    Author
    • Zeng, Wei. Future Cities Laboratory, Department of Architecture, ETH Zurich, 8093, Zurich, Switzerland
    • Huang, Xianfeng. Future Cities Laboratory, Department of Architecture, ETH Zurich, 8093, Zurich, Switzerland
    • Müller Arisona, Stefan. Future Cities Laboratory, Department of Architecture, ETH Zurich, 8093, Zurich, Switzerland
    • McLoughlin, Ian. University of Science and Technology of China, Hefei, China
    Document Type
    • Postprint
    OAI-PMH Identifier
    • oai:doc.rero.ch:324994
    Summary
    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%.