Research has been conducted on detecting dog behavior like sitting, walking, or running, to identify behavioral states and actions like barking, growling, howling, or whining to identify emotional states. Recently, an increase in the number of single-person households has led to studies on the behavior and control of companion animals, specifically the use of IoT sensor technology for the management of pet dogs. In particular, parallel processing techniques, cloud computing technology, research for providing real-time services to users, and encryption have been actively investigated to find ways to more efficiently process large amounts of data. Research on processing and analyzing big data in the IoT (Internet of Things) field has attracted considerable attention lately. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification). To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. This includes tasks such as automatic feeding, operation of play equipment, and location detection. The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing.
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