For this purpose, we acquire only intensity data by using a relatively resource-efficient noise sensor. In this paper, we propose a way to efficiently classify pet dog sound events using intensity data and long short-term memory-fully convolutional networks (LSTM-FCN) based on time-series data. Because data transfer and battery consumption are major issues for such sensors (sound and transmission sensors), which tend to have limitations on their capabilities, we need a way to classify pet dog sound events for resource-limited sensor devices. In general, to classify the sound events, sound data are acquired by sound sensors, pre-processed in various ways to perform tasks like feature extraction, and then classified. With regard to understanding the emotions of pet dogs, sound events provide the most important information, which is why sound sensors are widely used. Techniques have been developed for analyzing pet dog behavior to understand the emotional states, like depression or separation anxiety, of pet dogs who spend their time alone at home. For example, one study was conducted on pet dog health management that involved the detection of pet dog behaviors by means of acceleration sensors and heart rate sensors to identify food intake and/or bowel movements. 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. Get a lifetime subscription to VPN Unlimited for all your devices with a one-time purchase from the new Gadget Hacks Shop, and watch Hulu or Netflix without regional restrictions, increase security when browsing on public networks, and more.The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. Keep Your Connection Secure Without a Monthly Bill.
Sound normalizer arduino code#
You can find more details on the setup plus links to the code and remote control protocols on his tutorial.ĭon't want to build your own? You can buy an automatic volume controller to do it for you for about 20 bucks. Eventually, Bitar also wants to add the ability to automatically increase the volume again after the commercial and add support for more television brands.
You can tweak the code to determine your exact volume threshold and how fast you want the volume to change (gradually or all at once).
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