The escalating rates of gun-related violence and mass shootings represent a significant threat to public safety. Timely and accurate information for law enforcement agencies is crucial in mitigating these incidents. Current commercial gunshot detection systems, while effective, often come with prohibitive costs. This research explores a cost-effective alternative by leveraging acoustic analysis of gunshot recordings, potentially obtainable from ubiquitous devices like cell phones, to not only detect gunshots but also classify the type of firearm used. This paper details a study on deciphering gun type hierarchies using a curated dataset of 3459 recordings. We investigate the fundamental acoustic characteristics of gunshots, including muzzle blasts and shockwaves, which vary based on firearm type, ammunition, and shooting direction. We propose and evaluate machine learning frameworks, including Support Vector Machines (SVMs) as a baseline and a more advanced Convolutional Neural Network (CNN) architecture for joint gunshot detection and gun type classification. Results indicate that our deep learning approach achieves a mean average precision (mAP) of 0.58 on clean labeled data, outperforming the SVM baseline (mAP 0.39). Challenges related to data quality, environmental noise, and the generalization capabilities when using noisy web-sourced data (mAP 0.35) are also discussed. The long-term vision is to develop a highly accurate, real-time system deployable on common recording devices, significantly reducing detection costs and providing critical intelligence to first responders.

Joseph Voigt, 23, fled after leaving Marvin Voigt, 63, dead and Susan Voigt, 58, with a gunshot wound to the head. Police responded to the scene after Susan Voigt reported the incident at around 11:20 p.m. on Saturday. The Bartow Police Department said they arrived to find Marvin Voigt dead in the driveway from apparent gunshot wounds and Susan Voigt inside the home suffering from a serious gunshot wound. She was taken to a hospital in critical condition. "They found the mother sitting up on the couch," police chief Stephen Walker told reporters, according to Fox 13. "She was alive. She had been shot in the head once."

The New York police department (NYPD) announced it will begin using drones to respond to reports of robberies and alerts from a city-wide gunshot detection system. The drones will fly to the scene, piloted by an NYPD officer, and record video and audio that will be sent to police officers' smartphones in real time, according to a press release. The integration of these two surveillance technologies is part of a broader "Drone as First Responder" program that has existed since 2018. The New York city mayor, Eric Adams, and the city's interim police commissioner, Tom Donlan, announced the expansion on Wednesday afternoon. It will be initially rolled out to five precincts in Brooklyn, the Bronx and Manhattan.

Recently, deep learning-based methods have drawn huge attention due to their simple yet high performance without domain knowledge in sound classification and localization tasks. However, a lack of gun sounds in existing datasets has been a major obstacle to implementing a support system to spot criminals from their gunshots by leveraging deep learning models. Since the occurrence of gunshot is rare and unpredictable, it is impractical to collect gun sounds in the real world. As an alternative, gun sounds can be obtained from an FPS game that is designed to mimic real-world warfare. The recent FPS game offers a realistic environment where we can safely collect gunshot data while simulating even dangerous situations. By exploiting the advantage of the game environment, we construct a gunshot dataset, namely BGG, for the firearm classification and gunshot localization tasks. The BGG dataset consists of 37 different types of firearms, distances, and directions between the sound source and a receiver. We carefully verify that the in-game gunshot data has sufficient information to identify the location and type of gunshots by training several sound classification and localization baselines on the BGG dataset. Afterward, we demonstrate that the accuracy of real-world firearm classification and localization tasks can be enhanced by utilizing the BGG dataset.

Electronic shot counters allow armourers to perform preventive and predictive maintenance based on quantitative measurements, improving reliability, reducing the frequency of accidents, and reducing maintenance costs. To answer a market pressure for both low lead time to market and increased customisation, we aim to solve the shot detection and shot counting problem in a generic way through machine learning. In this study, we describe a method allowing one to construct a dataset with minimal labelling effort by only requiring the total number of shots fired in a time series. To our knowledge, this is the first study to propose a technique, based on learning from label proportions, that is able to exploit these weak labels to derive an instance-level classifier able to solve the counting problem and the more general discrimination problem. We also show that this technique can be deployed in heavily constrained microcontrollers while still providing hard real-time (<100ms) inference. We evaluate our technique against a state-of-the-art unsupervised algorithm and show a sizeable improvement, suggesting that the information from the weak labels is successfully leveraged. Finally, we evaluate our technique against human-generated state-of-the-art algorithms and show that it provides comparable performance and significantly outperforms them in some offline and real-world benchmarks.

A photographer used artificial intelligence to bring stars who left us too soon back to life - creating eerie portraits of Princess Diana, Kurt Cobain, John Lennon, Janis Joplin, Freddie Mercury and others. The haunting and realistic images are the work of Alper Yesiltas, a photographer based in Turkey, created the portraits for a project titled'As If Nothing Happened.' He used artificial intelligence photo enhancer software and photo editing programs to create the pictures. 'With the development of AI technology, I've been excited for a while, thinking that "anything imaginable can be shown in reality,"' Yesiltas wrote about the project. The haunting and realistic images are the work of Alper Yesiltas, a photographer based in Turkey, created the portraits for a project titled'As If Nothing Happened.' 'With the development of AI technology, I've been excited for a while, thinking that "anything imaginable can be shown in reality,"' Yesiltas wrote about the project.

Rai already has a product called Jarvis that is used by Uttar Pradesh Police and other state police forces, featuring closed circuit cameras (CCTVs) and artificial intelligence (AI)-based facial recognition. In its new edition, Jarvis doesn't just use cameras to watch crimes happen, it also employs microphones to listen to what's going on in the city. "We have used audio analytics to detect incidents such as prison fights in Uttar Pradesh. Our target is to implement it in smart cities," said Rai. The audio analytics tool is also being used by organizations in retail and manufacturing to detect distress sounds and accidents.

It is certainly startling when you hear a loud bang that sounds like a gunshot. Imagine sitting in your living room and from outside comes that blaring sound. It seemed like gunfire, but you aren't quite sure. There was a very loud noise, it was a sharp popping sound reminiscent of a gunshot, and it was close enough to be heard. Luckily, the sound wasn't so close that it might have been gunfire directed at or particularly nearby your home. Should you call the police to notify them about the apparent gunfire? Sometimes, people that think they might have heard a gunshot are reluctant to report that they heard the sound. One form of reluctance is due to their being unsure of what the sound really was. You don't want to officially claim that there was a gunshot if the sound turns out to be fireworks, or maybe a car that backfired making an abrupt boom.

Michael Williams' wife pleaded with him to remember their fishing trips with the grandchildren, how he used to braid her hair, anything to jar him back to his world outside the concrete walls of Cook County Jail. His three daily calls to her had become a lifeline, but when they dwindled to two, then one, then only a few a week, the 65-year-old Williams felt he couldn't go on. He made plans to take his life with a stash of pills he had stockpiled in his dormitory. Williams was jailed last August, accused of killing a young man from the neighborhood who asked him for a ride during a night of unrest over police brutality in May. But the key evidence against Williams didn't come from an eyewitness or an informant; it came from a clip of noiseless security video showing a car driving through an intersection, and a loud bang picked up by a network of surveillance microphones. Prosecutors said technology powered by a secret algorithm that analyzed noises detected by the sensors indicated Williams shot and killed the man. "I kept trying to figure out, how can they get away with using the technology like that against me?" said Williams, speaking publicly for the first time about his ordeal. Williams sat behind bars for nearly a year before a judge dismissed the case against him last month at the request of prosecutors, who said they had insufficient evidence.

Police can arrive at a scene where shooting is occurring without being called or without any officers observing the firing. What is the best way to accomplish this? With the assistance of AI technology, the answer is yes. Sensors, for example, can be put in municipal infrastructure. The sensors will be linked to a cloud-based program that will be able to correctly identify and pinpoint gunshots.