Department Publications

The Gaussian Mixture Model (GMM) based multi-stage Wiener filter (WF), recently introduced as a single-channel noise suppression method, demonstrates promising performance when applied to low signal-to-noise ratio (SNR) speech enhancement. This paper introduces a method for phonocardiogram (PCG) signal denoising using a two-stage subspace adaptive filtering method, with the aim of improving the noise suppression performance using GMM WF. Specifically, we utilize the normalized least mean squares (NLMS) and GMM WF to propose a hybrid adaptive filter with multi-channel spectral principal components analysis (PCA), which suppresses residual noise. We perform experimental evaluations on two heart sound datasets and show that the proposed denoising method exhibits increased performance over baseline approaches under various real-world noise conditions.

In coastal environments, detecting and classifying low radar cross-section (RCS) moving targets such as dinghies and trawlers using pulse radar system is challenging due to strong sea and land clutter. The clutter reduces detection accuracy. Further, the detection and classification heavily depend on radar operator expertise. To overcome these limitations, we propose an operator-independent framework for detection and classification. This paper presents a hybrid approach that combines Robust Principal Component Analysis (RPCA) with deep learning-based object detection models to improve target detection using radar Plan Position Indicator (PPI) images. RPCA is first applied as a low rank nature clutter separation from sparse moving targets. Detection is then performed using You Only Look Once (YOLO) and Detection Transformer (DETR) models on these clutter-suppressed representations. Experimental …