Deep Learning-Based Dog Behavioral Analysis from the Audio Data Using MFCC Feature Extraction

Resumen

Understanding the behavior and emotions of dogs through their vocalizations is vital for various applications that includes emotional intelligence analysis and remote monitoring services. In this study, the vocal behavior of Dachshund dogs has been examined by using a novel feature extraction technique based on Mel Frequency Cepstrum Coefficients (MFCC) and advanced Deep Learning architectures. Classifying the vocalizations of pets accurately, particularly when they are left alone, is a critical challenge. Hence, an approach is offered in this paper that integrates MFCC-based feature extraction with multiple neural network architectures like CNN, LSTM, Feedforward Networks (FFN), and hybrid CNN-LSTM models. In comparison to the other architectures, the Stacked CNN-BiLSTM model outperforms better at detecting Pet Sound Events. This study enhances pet dog vocalization analysis, enabling better communication in the field.