KCI is pleased to announce the immediate availability of SoundScanNX, an advanced machine learning model that streamlines environmental noise classification, transforming how engineers and urban planners assess traffic sounds and their impact on the community.
SoundScanNX brings efficiency, objectivity, and precision to a process traditionally reliant on time-consuming manual analysis. For use in traffic noise impact studies, this AI-driven model simplifies the classification of audio recordings, automatically distinguishing between traffic and non-traffic noise, such as cars, motorcycles, trucks, airplanes, birds, voices, and more. By eliminating the need for human listening, SoundScanNX streamlines traffic studies by automatically analyzing audio recordings, identifying the dominant sound in each file along with its peak and average amplitudes. SoundScanNX is available on the BRYX Model-as-a-Service (MaaS) cloud platform, allowing users to quickly upload their audio files, process them efficiently, and easily view and download their results.
“Accurate and consistent noise classification is essential for shaping healthier, more livable communities. SoundScanNX empowers AEC professionals with the insights they need to design and recommend smarter urban spaces while optimizing resources and ensuring regulatory compliance.”
– Jeanne Ruthloff, Technology & Innovation Sector President
Designed for easy integration into existing urban planning workflows, SoundScanNX eliminates tedious data sorting, allowing professionals to focus on strategic decisions. This advancement helps streamline workflows, reduce inefficiencies, and support data-driven decisions for sustainable urban development. To get started with a fully functional free trial of the SoundScanNX model, visit gobryx.com.