## 3 Approach and Analysis: Architecture and Methodology
### Learning Algorithm for Sound Classification  
In the sound classification process, the pattern of dog barking sound identification was made based on the labelling information from the sound recorded. Acoustic features were taken into consideration for classifying context-based dog sounds. A model was designed to identify the context of the dog barking sound by utilizing the extracted features. A classification method was applied for learning the models from the data. These models were used for the prediction of dog behavior based on the acoustic features from the individual contexts. Figure 4: Reinforcement learning method for dog sound predictions. In the sound classification problem, feature vector \(\kappa\in\) Rn and its components, x1... xn, are called predictor variables, and there is a class variable C taking values on (0,1). The objective is to construct classifier models from the training data consisting of a set of 'n' observations, Dn = ((x1, C1), (x2, C2)...... (xn, Cn)), from the probability distribution \(p\)(x, C). The sound classification model used to assign labels to different instances, x(n + 1), is characterized by the predictor variables. The integrity of the model for sound prediction and classification depends on the data values of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). The error rate can be defined as (FN + FP)/N, and the accuracy as (TP + TN)/N. N is the total number of instances of TP, FP, TN, and FN. Table 2 represents the dog sounds recorded and their prediction accuracy. The total number of instances taken for the accuracy prediction is 800. The differences in the predictions and error rate will be improved using the proposed approach. The Q-learning algorithm is the descriptive form of reinforcement learning algorithms. Q-learning can learn an efficient technique, even without an operating prototype, by adjusting the reward and action of a state called the Q function. It will find the next best action for the given current state and aims to choose the action at random and maximizes the reward. Q-learning was used in the sound predictions because of its computational efficiency in learning. It is a learning algorithm that will return the highest Q value for each state, resulting in a search for optimal actions. The action value of the function [43] can be written as:  
\[\text{Q}_{\text{t+1}}\left(\text{s}_{\text{t}},\text{y}_{\text{t}}\right)= \text{Q}_{\text{t}}\left(\text{s}_{\text{t}},\text{y}_{\text{t}}\right)+ \propto_{\text{t}}\left[\text{r}_{\text{t+1}}+\beta\times\text{max}_{\text{a }\in\text{A}}\text{Q}_{\text{t}}\left(\text{s}_{\text{t+1}},\text{y}\right)- \text{Q}_{\text{t}}\left(\text{s}_{\text{t}},\text{y}_{\text{t}}\right)\right] \tag{1}\]  
The above equation represents the Temporal Difference (TD) update, which was applied every time a sound was detected in the environment. This updating was used in the best possible prediction of the sounds acquired. The algorithm A1- learning algorithm describes the learning method of the proposed approach, with the different states and their actions. Q (s, a) is the expected value of doing a in state s, following the optimal policy. The Q-table is initialized, and an action is chosen for analysis. For each action, a reward is returned to determine the action. The Q-table is updated with the learning function for different actions. The k-nearest neighbor classifier was used in the prediction of the sounds of the dog based on the given instance. It determines the sounds labelled 'x' for the most frequent sounds found in the 'k' instances. The Euclidean distance algorithm was used in the estimation of the nearest neighbors for the continuous variable 'x'. The prediction process was conducted by using the labels of the k-nearest neighbor in the sounds. For the model design, the wrapper selection method was used in the feature selection process in classifying the sounds. It is useful for making the automatic selection of data that are most relevant to the model design predicted.