title: Automatic Detection of Cow's Oestrus in Audio Surveillance System.
abstract: Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.
authors: Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea.
authors: Y
authors: Y
authors: Chung
authors: Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea.
authors: J
authors: J
authors: Lee
authors: Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea.
authors: S
authors: S
authors: Oh
authors: Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea.
authors: D
authors: D
authors: Park
authors: Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea.
authors: H H
authors: HH
authors: Chang
authors: Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea.
authors: S
authors: S
authors: Kim
keywords: Cow’s Oestrus Detection; Feature Subset Selection; Mel Frequency Cepstrum Coefficient; Sound Data; Support Vector Data Description