## 1 Introduction
Many research works have focused on the machine learning approach to explore the context of dog sounds. Machine learning models have the ability to learn and improve from experiences [32]. The classification and prediction of complex information has been achieved efficiently using machine learning techniques [33]. Deep learning has upgraded the learning architecture of machine learning [34]. Machine learning techniques have been used in designing an automated system [35]. Reinforcement learning is an area of machine learning used in various scenarios to identify possible behavior in a given situation. Reinforcement learning creates a policy for the given action-value function based on the behavior observed. In the training process, regularization has an important role in adjusting the hyperparameters, such as environment, for analyzing different scenarios [36]. For improving the generalizability of the learning method, efficient regularization needs to be designed. Regularization is required in complex environments to overcome problems such as overfitting during the training phase [37; 38; 39]. In the proposed work, the environment is simple, comprising dog and owner; hence, there will be lower complexity. The machine learning approach can be used in identifying dog characteristics based on analyzing dog acoustic features. This can be attained by analyzing the numerous sounds of the dog in different situations. Furthermore, machine learning approaches have been used to classify sounds from other animals, such as elephants, dolphins, birds, etc. [40]. Dogs make sounds for conveying information, and they are of a certain pattern. Dogs have a pattern of barking relating to a specific situation, such as stranger detection, walking, playing, etc. [41]. In this study, two dog types from the southern part Tamil Nadu have been selected for the analysis of dog barking pattern and sound analysis; prediction analysis and classification of dog barks on the Rajapalayam and Kombai Hound dog types have been carried out. Training on barking pattern was given to the dogs for detecting the presence of a known person or a stranger to the house. The results of machine learning approach analysis showed that the barking of the dog carried context information features relating to the identification of known and unknown persons in an effective way. Acoustic analysis through machine learning is required to explore the data from the barking sounds, and the context can then be closely identified. This article focuses on the analysis of dog sound patterns to predict the context of the sound produced and train the dogs specifically to the contexts. The sounds of dog barking during different scenarios were analyzed. Analysis of dog barking pattern was performed in order to extract the features and context information of dog barks. On extracting these features, the dog barking was trained with the machine learning algorithm to propose an automated system for movement detection. The overall structure of the paper is organized as follows: data collection and analysis are detailed in Section 2. In Section 3, the architecture and methodology of the dog sound classification with the learning algorithm is explained. The results of the proposed work are discussed in Section 4. Section 5 concludes the work.