Data Collection and Analysis for Automatically Generating Record of Human Behaviors by Environmental Sound Recognition

  • Takahiro FuruyaEmail author
  • Yuya Chiba
  • Takashi Nose
  • Akinori Ito
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 110)


Nowadays, the “life-log,” recording our daily activities using a camera, microphone or other sensors and retrieve those recorded data, is becoming more and more realistic. One of the applications that utilize the life-log data is the automatic generation of activity summary of the user. The present work focuses on using sound data to make the activity summary. There have been several works that classified the recorded sound based on the user’s activity. The focus of those studies was how to classify the collected data into a pre-defined set of activity classes. However, there have been no considerations what kind of activity classes were appropriate for this purpose. Moreover, we need a basic investigation for optimizing parameters of sound recognition such as window size for feature calculation. Therefore, we first investigated the optimum parameters for feature extraction, and then analyzed the acoustic similarities of sound features observed by various activities. We exploited twenty-two hours of environmental sound in a test subject’s ordinal life as the training and test data. Using the data, we analyzed the acoustic similarities of the activity sound using hierarchical clustering. As a consequence, we observed that target classes could be classified into three groups (“speaking,” “silent” and “noisy”). Misrecognitions between those groups were rare, and we observed a large number of misrecognitions within the “speaking” group.


Environmental sound recognition Hierarchical clustering Neural network 


  1. 1.
    Hori, T., Aizawa, K.: Context-based video retrieval system for the life-log applications. In: Proceedings the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 31–38 (2003)Google Scholar
  2. 2.
    Park, H.-S., Cho, S.-B.: A personalized summarization of video life-logs from an indoor multi-camera system using a fuzzy rule-based system with domain knowledge. Inf. Sys. 36(8), 1124–1134 (2011)CrossRefGoogle Scholar
  3. 3.
    Wang, P., Smeaton, A.F.: Using visual lifelogs to automatically characterize everyday activities. Inf. Sci. 230, 147–161 (2013)CrossRefGoogle Scholar
  4. 4.
    Lee, M.-W., Khan, A.M., Kim, T.-S.: A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation. Pers. Ubiquit. Comput. 15(8), 887–898 (2011)CrossRefGoogle Scholar
  5. 5.
    Su, X., Tong, H., Ji, P.: Activity recognition with smartphone sensors. Tsinghua Sci. Technol. 19(3), 235–249 (2014)CrossRefGoogle Scholar
  6. 6.
    Hayashi, T., Nishida, M., Kitaoka, N., Takeda, K.: Daily activity recognition based on DNN using environmental sound and acceleration signals. In: 23rd European Signal Processing Conference (EUSIPCO), pp. 2306–2310 (2015)Google Scholar
  7. 7.
    Trilsbeek, P.: ELAN, The Language Archive. Accessed 1 Oct 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Takahiro Furuya
    • 1
    Email author
  • Yuya Chiba
    • 1
  • Takashi Nose
    • 1
  • Akinori Ito
    • 1
  1. 1.Tohoku UniversitySendaiJapan

Personalised recommendations