CLASSIFICATION OF CAT SOUNDS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND LONG SHORT-TERM MEMORY (LSTM) METHODS

Fadhilah Gusti Safinatunnajah; Agi Prasetiadi; Merlinda Wibowo

doi:10.20884/1.jutif.2022.3.5.373

Fadhilah Gusti Safinatunnajah Program Studi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto, Indonesia
Agi Prasetiadi Program Studi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto, Indonesia
Merlinda Wibowo Program Studi Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.5.373

Keywords: Cat, CNN, LSTM, Voice

Abstract

Cats become pets who are very close to humans, and they convey messages by producing identical sounds. Therefore, analysis of pet voices is important for a better relationship between cats and human. Animal communication through sound, especially in cats, depends on the situation or context in which the sound is made such as in a state of danger. Based on these problems, a classification method is needed to classify the similarity of characteristics in the resulting sound pattern. The classification methods used are Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) which can remember information for a long time and are used for a long time period. This study aimed to determine feelings or moods based on the sound produced into 4 categories: The Purr, The Meow, The Mating Call, and The Howl. The result of this study is that the best architectural model is to use 4 CNN convolution layers measuring 8-8-8-8 and 2 LSTM layers measuring 8-8. The precision value in this architecture is 0.68, the recall value is 1.00, the accurary value is 0.5625 and the f1-score value is 0.77. The small value of the confusion matrix is caused by the lack of dataset duration in the training process, resulting in underfitting.

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