<![CDATA[In everyday life, pets such as dogs often become an inseparable part of human life. Motivations for keeping a pet can vary from individual to individual, ranging from the need for a loyal companion to the responsibility of caring for another living creature. Among the various choices of pets, dogs are often considered the most loyal and loyal friends towards humans. This uniqueness makes many people choose to keep dogs as part of their family. Often, dog owners may not understand the message that the sounds produced by their beloved pets are trying to convey. These dog sounds have a special purpose that can reflect various emotions, such as joy, sadness, or anger. A dog's voice can also be an indicator of their health that owners need to pay attention to. The main focus of this research is to develop dog voice classification technology to help owners understand and communicate with their pet dogs. In this research, a pre-trained YAMNet model is used as a basis for classifying various audio events. The model training process uses the CNN algorithm contained in the YAMNet architecture. The total data used was 373 data which were classified into 4 classes, namely, bark, howling, growling, whimper. The results of this research model achieved 97.8% accuracy with precision, recall and f1-scores for each class >= 95%.]]>
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