Automatic Detection of Pig Sneeze Using a Small Size Acoustic Features Detectable in a Different Recording Environment

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  • MITO Misaki
    Graduate School of Systems and Information Engineering, University of Tsukuba
  • AOKI Takuya
    Graduate School of Systems and Information Engineering, University of Tsukuba
  • KAWAGISHI Takuji
    Graduate School of Systems and Information Engineering, University of Tsukuba
  • MIZUTANI Koichi
    Graduate School of Systems and Information Engineering, University of Tsukuba Acoustic Laboratory, Faculty of Engineering, Information and Systems, Division of Engineering Interaction Technologies, University of Tsukuba
  • ZEMPO Keiichi
    Graduate School of Systems and Information Engineering, University of Tsukuba Acoustic Laboratory, Faculty of Engineering, Information and Systems, Division of Engineering Interaction Technologies, University of Tsukuba
  • WAKATSUKI Naoto
    Graduate School of Systems and Information Engineering, University of Tsukuba Acoustic Laboratory, Faculty of Engineering, Information and Systems, Division of Engineering Interaction Technologies, University of Tsukuba
  • MAEDA Yuka
    Graduate School of Systems and Information Engineering, University of Tsukuba Acoustic Laboratory, Faculty of Engineering, Information and Systems, Division of Engineering Interaction Technologies, University of Tsukuba
  • TAKEMAE Nobuhiro
    National Institute of Animal Health, NARO
  • SAITO Takehiko
    National Institute of Animal Health, NARO

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Other Title
  • 異なる収録環境で検出可能な少数の音響特徴量を用いる豚くしゃみの自動検出
  • コトナル シュウロク カンキョウ デ ケンシュツ カノウ ナ ショウスウ ノ オンキョウ トクチョウリョウ オ モチイル ブタ クシャミ ノ ジドウ ケンシュツ

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Description

The number of sneezing increase as swine influenza infection symptom in an early stage. Collecting many sneezing sounds of infected pigs is hard; sneezing classifier used small size acoustic features is necessary. In previous research, F-measure (of classifying accuracy) was about 60 % only, moreover, comparative evaluation has not conducted in a different environment and different acoustic features. The purpose of this paper is developing a pig sneezing classifier detectable in a different recording environment on high performance. We recorded a video and acoustic signal in multiple positions for 2 weeks after we infected pigs with swine influenza. In the experiment, we used multiple kinds of influenza virus. From the recorded acoustic signal, we detected 74533 samples of acoustic events automatically under a decided detection level. We assigned labels using with a movie for a part of acoustic events; we collected acoustic events including 144 sneezes. For acoustic events, we extracted a variety of acoustic features, and we evaluated classification performance using a classifier based on Support Vector Machine. As a result, developed classifier’s F-measure is 92.8 %, and it is very higher than the previous method. In this case, the classifier’s acoustic features are Mel Frequency Cepstral Coefficients, a feature explained spectral rising, and frequency change in a low-frequency band. In addition, trained classifier detected 3764 sneezes. Consequently, we developed high-performance sneezing classifier using small size acoustic features for detectable in a different recording environment.

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