Detection and Discrimination of Sniffing and Panting Sounds of Dogs

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Detection and Discrimination of Sniffing and Panting Sounds of DogsOphir Azulai(1), Gil Bloch(1), Yizhar Lavner (1,2), Irit Gazit (3) and Joseph Terkel (3).Signals and…
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Detection and Discrimination of Sniffing and Panting Sounds of Dogs
  • Ophir Azulai(1), Gil Bloch(1), Yizhar Lavner (1,2), Irit Gazit (3) and Joseph Terkel (3).
  • Signals and Image Processing Lab (SIPL), Faculty of Electrical Engineering, Technion IIT, Haifa, Israel (http://www-sipl.technion.ac.il)
  • Computer Science, Tel-Hai Academic College, Upper Galilee, Israel.
  • Department of Zoology, George S. Wise Faculty of Life Sciences Tel-Aviv University, Tel Aviv 69978, Israel.
  • AbstractDogs’ acute sense of smell enables them to accurately identify many types of odors. This ability serves in the detection of drugs, explosives, finding people, and other special tasks. The possibility of controlling dogs’ behavior suggests many potential applications.For this purpose, an automatic system for recognition of panting and sniffing according to their corresponding acoustic signals was developed. The system consists of three main components: analysis, training and discrimination. In the analysis stage the events were detected using short-time energy and zero-crossing rate functions. Features for discrimination were extracted utilizing an autoregressive model or by means of a Mel-frequency cepstrum. A few parameters of the spectral envelope were selected, based on their discrimination ability. In the training stage, a feedforward neural network was trained, using labeled signals. Alternatively, a fuzzy K-nearest neighbor algorithm can be used. Correct identification of up to 96.8% was achieved in the discrimination stage.Panting and Sniffing SignalsDiscrimination Method 1Events findingUsing Normalized Short Time Energy FunctionAR model frequency responseNeural NetworkUsing three layers, feed forward network with back propagation learning methodDiscrimination Method 2Mel-Frequency Cepstral CoefficientsEvent Feature VectorFuzzy KNNMembership FunctionClassification results using Fuzzy KNNComparison of both methodsCalibration Process:Comparison of both methods continuedAnalyze Process:Conclusions
  • Sniffing and Panting sounds has unique signature that can discriminate between them
  • Some frequency response model can give us the desired information.
  • Event finding should combine both feature vector and some other method like energy function or ZCR.
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