Effective sound detection system in commercial car vehicles using Msp430 launchpad development

In vehicular networks, several automation protocols are invented in artificial intelligence-based systematic processes. But best of our knowledge, none of the methods discussed effective sound detection systems and sound transducers based on real-time scenarios. In this research, an effective sound detection system and sound transducer for an intelligent sound adjustment system in commercial car vehicles using proposed sound prototyping development are developed to create an impact of a sound detection system. This Intelligent sound adjustment system enables six models for vehicle wearable sensor systems with Value Line MSP430 LaunchPad™ Development Kit. This model reduces and maintains a balanced sound system inside the car based on unique circumstances such as Acoustic source localization, Microphone with Super cardioid, and Mass comparison. This Proposed Analysis of an Intelligent sound adjustment system in commercial car vehicles works based on Value Line MSP430 LaunchPad™ Development Kit connected with "Acoustic source localization," "Microphone with Super cardioid" for creating a vehicle wearable sensor system. This system can find a nearby vehicle, especially an ambulance, and the siren sound of an ambulance. However, sensors are car wearable devices for accessing ambulance sound and predicting exclusive sound patterns of an ambulance by comparing and predicting sound from a real-time sound to a sound database. Proposed connected development kit to sound system used to control sound. According to experimental findings, an effective sound detection system in commercial car vehicles using proposed sound prototyping achieves a recognition accuracy equivalent to that of edge Value Line MSP430 LaunchPad™ Development systems already in use while significantly decreasing data traffic by 93.5 and computational delay by 92%. Findings indicate that Intelligent sound adjustment system fault detection accuracy ranges from 99.82 percent on average to 97.92 percent at its peak. Compared to traditional federated learning, a microphone with super-cardioid considerably reduces energy usage by 68.5 with a minimum accuracy loss of up to 97.3%. Acoustic source localization is built and utilized to find sound faults at the end. This technique offers a fresh concept for arc sound detection systems and performs well.