A Cost-Effective Recursive Adaptive Filter Setup for Reducing Vehicle Interior Sound

The ability to drive is significantly impacted by noise in a car; for example, it can be distracting and, over time, cause health problems. Therefore, it’s essential to reduce internal vehicle noise adequately. In telecommunication and biomedical engineering noise is minimized using filtering performed adaptively. This research introduces a switching-based adaptive filtering technique to minimize the vehicle interior noise. The recursive switched structure suggests a two-stage filter structure, with the adaption method changing at each stage. The speech from the NOIZEUS dataset tainted by interior vehicle noise from the VISC dataset is applied to the switched recursive filter structure for its evaluation. Simulation findings confirm the suggested filter model’s astonishingly good performance; and there is an SNR and ANR improvement of 20–30% concerning various filters. The MSE values are reduced by 50–90% using the proposed filter model. An adaptive filtering technique’s essential advantage is providing a simple and cost-effective ANC model for signal denoising. The normalized and error-normalized algorithms utilized in the proposed filter led to optimized step size selection for the filter.