<p>This research presents a robust convolutional neural network (CNN)-based technique for identifying the ambience of acoustic communication. The study is motivated by the need for accurate categorization of acoustic environments, which has applications in various fields such as security, entertainment, and health monitoring. The purpose of this research is to develop a model that can classify acoustic communications into specific emotional categories with high accuracy. Initially, preprocessing steps are applied to the acoustic communication data, including lowercase conversion, tokenization, padding sequence, and word embedding. These steps produce a feature matrix that serves as the input for a multi-level CNN. The proposed model is designed so that each CNN layer reduces the input matrix to half its size, with the output of one layer being combined with the next through average pooling. This process, referred to as a global pass, is repeated across multiple blocks of the same architecture. The concatenated global outputs and the output from the previous CNN layer are then passed into two fully connected layers with 512 and 8 neurons, respectively. The final output from the second fully connected layer (FC(8)) provides the probabilities for the communication to fall into one of the eight predefined categories: tensed, peaceful, chaotic, joyful, sorrowful, excited, religious, and unhealthy. The proposed model demonstrates superior performance with a 95.90% accuracy rate, outperforming state-of-the-art machine learning techniques. The comparative analysis shows highly encouraging results, suggesting the effectiveness of the model in practical applications. This study contributes to the advancement of acoustic communication analysis by providing a reliable and efficient classification method.</p>