Vehicle robbery and unknown car thefts has become a intense issue around the nation. Many culprits use unapproved vehicles to perform numerous illegal activities and leave the vehicles. The utmost reason for accidents is due to the vehicles driven by unknown users, who perform reckless and inexperienced driving without the speed limit will cause many accidents that increases the death rate. Our goal is to make a system which will allow the person who have authorized license. For this purpose, we plan to install an automated system in the vehicle to introduce smart license verification technology. Various techniques and technologies are being explained to detect the details of the driver, and also Various vehicle thefts are being done in spite of various surveillance cameras are set down to keep an eye on the activities and various technologies are being implemented to diminish the vehicle robbery. So, we proposed the system with the concept of deep learning. As compared to normal detection techniques deep learning collects N number of input samples and compares it with the database details. After the authentication process the engine mechanism starts, if not authorized it gives a buzzer sound and vehicle doesn’t start until the details of registered person is authenticated.

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Techniques for reducing noise in an ever-increasingly noisy world are essential. It is the purpose of this paper to review and demonstrate the efficacy of active noise cancellation in speech signals. Acoustic noise reduction is used in industrial, manufacturing, and consumer products, such as mobile phones. -Adaptive filtering, zero frequency filtering, and its advantages and disadvantages are all included here. Other than what's already been mentioned and put to the test with a noisy signal, an effective approach is developed and the results are reported.