A global automotive manufacturer needed in-car voice commands to stay accurate over engine and road noise. We built an ML-driven adaptive filtering system, now in final pre-production testing for a new vehicle line.
Voice control degraded badly under real driving conditions — engine, road and wind noise pushed recognition error rates up and frustrated drivers. Fixed filters could not adapt to changing acoustic conditions.
Machine-learning-driven correction of IIR/FIR filter coefficients that adapts to changing cabin noise in real time.
Models trained across engine, road and wind-noise profiles representative of real driving.
Low-latency inference within automotive compute constraints, suitable for embedded deployment.
An evaluation harness measuring recognition accuracy across controlled noise scenarios for pre-production sign-off.
Figures reflect outcomes measured on this engagement. Client withheld under NDA.
We treated this as a signal-processing problem solved with ML rather than a generic model — adapting proven filtering to changing acoustics. The system is in final pre-production testing ahead of a new vehicle line.
We scope a clear plan with milestones and architecture options — and right-sized GPU hardware if AI workloads are involved.