Improved in-car sound pick-up using multichannel Wiener filter

With advancements in automotive electronics and sensors, the sound pick-up using multiple microphones has become feasible for hands-free telephony and voice command in-car applications. However, challenges remain in effectively processing multiple microphone signals due to bandwidth or processing limitations. This work explores the use of the Multichannel Wiener Filter algorithm with a two-microphone in-car system, to enhance speech quality for driver and passenger voice, i.e., to mitigate notch-filtering effects caused by echoes and improve background noise reduction. We evaluate its performance under various noise conditions using modern objective metrics like Deep Noise Suppression Mean Opinion Score. The effect of head movements of driver/passenger is also investigated. The proposed method is shown to provide significant improvements over a simple mixing of microphone signals.

Current-Steering DAC Architecture Design for Amplitude Mismatch Error Minimization

We propose a novel digital-to-analog converter (DAC) weighting architecture that statistically minimizes the distortion caused by random current mismatches. Unlike binary, thermometer-coded, and segmented DACs, the current weights of the proposed architecture are not an integer power of 2 or any other integer number. We present a heuristic algorithm for a static mapping of DAC input codewords into corresponding DAC switches. High-level Matlab simulations are performed to illustrate the static performance improvement over the segmented structure.

T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals

Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rain or snow. However, radar does possess traits that make it unsuitable for standard emission-based deep learning representations such as point clouds. Radar point clouds tend to be sparse and therefore information extraction is not efficient. To overcome this, more traditional digital signal processing pipelines were adapted to form inputs residing directly in the frequency domain via Fast Fourier Transforms. Commonly, three transformations were used to form Range-Azimuth-Doppler cubes in which deep learning algorithms could perform object detection. This too has drawbacks, namely the pre-processing costs associated with performing multiple Fourier Transforms and normalization. We explore the possibility of operating on raw radar inputs from analog to digital converters via the utilization of complex transformation layers. Moreover, we introduce hierarchical Swin Vision transformers to the field of radar object detection and show their capability to operate on inputs varying in pre-processing, along with different radar configurations, i.e. relatively low and high numbers of transmitters and receivers, while obtaining on par or better results than the state-of-the-art.