Real-Time Deployment of Machine Learning-Based Audio Analysis Systems for Suspicious Message Detection

In recent years, the proliferation of digital communication has made it easier for individuals to transmit audio messages in a variety of contexts. While this has facilitated many positive aspects of communication, it has also created new challenges for detecting suspicious activity that may pose a threat to security or privacy[1]. In response to these challenges, researchers have proposed various approaches to detect suspicious audio messages using machine learning techniques. This paper presents a novel approach to detecting suspicious audio messages that combine acoustic features and statistical models. We use machine learning algorithms to identify anomalies in audio messages that may indicate suspicious activity. The system analyzes various characteristics of the audio messages, including speech patterns, voice quality, background noise, and other acoustic features. We evaluate our approach on a dataset of real-world audio messages[2] and achieve promising results in terms of accuracy and efficiency. Our system can be used in a variety of applications, such as law enforcement, national security, and corporate communication monitoring. Our work contributes to the development of effective and reliable tools for detecting suspicious audio messages in today's digital age. By leveraging machine learning and acoustic analysis, our approach offers a valuable tool for ensuring security and privacy in audio communication.