Intelligent Acoustic Module for Autonomous Vehicles using Fast Gated Recurrent approach
This addresses sound identification and localization for autonomous vehicles, but it appears incremental as it builds on existing gated recurrent neural networks with noise reduction.
The paper tackles acoustic single and multi-tone classification for resource-constrained edge devices, achieving improved performance metrics and lower model size compared to previous methods.
This paper elucidates a model for acoustic single and multi-tone classification in resource constrained edge devices. The proposed model is of State-of-the-art Fast Accurate Stable Tiny Gated Recurrent Neural Network. This model has resulted in improved performance metrics and lower size compared to previous hypothesized methods by using lesser parameters with higher efficiency and employment of a noise reduction algorithm. The model is implemented as an acoustic AI module, focused for the application of sound identification, localization, and deployment on AI systems like that of an autonomous car. Further, the inclusion of localization techniques carries the potential of adding a new dimension to the multi-tone classifiers present in autonomous vehicles, as its demand increases in urban cities and developing countries in the future.