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

抄録

As an effort to realize a sustainable society, Tokai University Takanawa Campus has used a smart garbage collection service called BigBelly^TM 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.