Description of original award (Fiscal Year 2016, $93,561)
As submitted by the proposer:
Audio analysis of gunshots is based on the observation that the content and quality of gunshot recordings are influenced by firearm and ammunition type, the scene geometry, and the recording device used. Advanced computational techniques can exploit these facts to answer investigative questions. For example, in much the same way that signal processing for human speaker recognition can help reach conclusions regarding the gender, age, identity, or national origin of the speaker, it appears possible that similar methods may be able to answer firearm specific questions from audio data. To date, these advanced computational methods have not been applied to the audio analysis of gunshots. As more crimes are captured on audio recordings, more examiners will be asked to answer firearm-based questions. The proposed Aims include a significantly more mathematically rigorous approach than has been previously performed. We will use a fine-grained mathematical representation of the frequency spectrum and analyze this representation with a series of advanced machine learning techniques for clustering and pattern recognition.
Through the proposed aims we will assemble a large set of recorded gunshots, make these recordings available to the research community, and develop advanced algorithms to provide investigative insight. We will develop algorithms to detect gunshots, differentiate muzzle blasts from shock waves, determine shot-to-shot timings, determine the number of firearms present, assign specific shots to firearms, and estimate probabilities of class and caliber. These aims represent a series of R&D steps towards the creation and validation of computational tools for the audio analysis of gunshots. The proposed work will develop analytic techniques, grounded in mathematical science and able to provide quantified answers to the audio analysis of gunshots. This should benefit law enforcement and their ability to utilize this important new source of evidence. With the increased prevalence of smartphones and body-cameras the work should impact investigators country-wide.
Note: This project contains a research and/or development component, as defined in applicable law.
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