Improved Feature Extraction Method for Sound Recognition Applied to Automatic Sorting of Recycling Wastes

• Tatsuji, Munaka

• Farzad, Samie

• Lars, Bauer

• Jörg, Henkel

抄録

As an effort to realize a sustainable society, Tokai University Takanawa Campus has used a smart garbage collection service called BigBellyTM since 2016 for recovering wastes such as cans, bottles, and plastic bottles for recycling. The objective of this demonstration experiment is to clarify how much waste has been correctly separated and collected. As a result of this demonstration about three years, it found that about 30% of the recycling wastes had not correctly sorted. To improve this situation, we propose an automatic sorting function using sound recognition. In many types of research for voice recognition, Mel Frequency Cepstral Coefficient (MFCC) has been used as an algorithm for extracting features used for machine learning Support Vector Machines (SVMs). One reason is that MFCC extracts valuable features that focus on the low dimension for the human voice. However, the sounds of recycling wastes have features of frequency components found in higher dimensions. Based on this characteristic, we propose an improved method of MFCC suitable for sounds, rather than voice recognition for identifying recycling wastes and show the results of the automatic sorting of recycling wastes.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.28(2020) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.28.658------------------------------

As an effort to realize a sustainable society, Tokai University Takanawa Campus has used a smart garbage collection service called BigBellyTM since 2016 for recovering wastes such as cans, bottles, and plastic bottles for recycling. The objective of this demonstration experiment is to clarify how much waste has been correctly separated and collected. As a result of this demonstration about three years, it found that about 30% of the recycling wastes had not correctly sorted. To improve this situation, we propose an automatic sorting function using sound recognition. In many types of research for voice recognition, Mel Frequency Cepstral Coefficient (MFCC) has been used as an algorithm for extracting features used for machine learning Support Vector Machines (SVMs). One reason is that MFCC extracts valuable features that focus on the low dimension for the human voice. However, the sounds of recycling wastes have features of frequency components found in higher dimensions. Based on this characteristic, we propose an improved method of MFCC suitable for sounds, rather than voice recognition for identifying recycling wastes and show the results of the automatic sorting of recycling wastes.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.28(2020) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.28.658------------------------------

収録刊行物

• 情報処理学会論文誌

  情報処理学会論文誌 61 (9), 2020-09-15

キーワード

• [特集：“Applications and the Internet” in Conjunction with Main Topics of COMPSAC2019] Internet of Things (IoT), edge computing, smart garbage collection services, sound recognition, feature extraction, Mel Frequency Cepstral Coefficient (MFCC), Support Vec

詳細情報

• CRID: 1050285700295890688
• NII論文ID: 170000183397
• NII書誌ID: AN00116647
• ISSN: 18827764
• Web Site: http://id.nii.ac.jp/1001/00206818/
• 本文言語コード: en
• 資料種別: journal article
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