Agenda

MSc SPS Thesis Presentation

• Wednesday, 3 September 2025
• 10:00-11:00
• HB17.140 (Seminar room)

Computationally-Efficient Sparsity-Aware Occupancy Grid Mapping for Automotive Driving

Occupancy maps are used in automotive driving applications to understand the scene around the vehicle using data from sensors like LiDAR and/or radar on vehicles. In state-of-the-art work, pattern-coupled sparse Bayesian learning (PCSBL) was used to estimate the occupancy map by leveraging spatial dependencies across grids in the map for both single modalities and the fusion of multiple modalities. The PCSBL method, however, has high computational complexity, making real-time implementation challenging for large-scale grid maps. To address this limitation, we propose several methods to improve the computational efficiency of PCSBL while maintaining mapping accuracy. First, we utilize a precomputed lookup table to accelerate selection matrix construction. Second, we implement adaptive resolution reduction based on sensor measurements. Third, we develop two novel methods that exploit the narrow angular interactions between measurements and the map regions to enhance computational efficiency. The first method partitions measurements into spatially disjoint submaps that enable parallel processing. The second method exploits the angular structure to impose a block structure on the selection matrix, reducing computational overhead. Experiments on the nuScenes and RADIATE public datasets show that the presented methods reduce computational costs compared to the benchmark PCSBL and fusionbased PCSBL methods while preserving detection accuracy.

Signal Processing Seminar

• Wednesday, 17 September 2025
• 13:30-17:00
• Snijderszaal (LB01.010)

Personalized Auditory Scene Modification to Assist Hearing Impaired People

This symposium is on the occasion of the PhD defense of Changheng Li on 18 September. During the symposium we have external speakers (Zheng-Hua Tan, Sharon Gannot) as well as speakers from within our NWO/TTW project "Personalized Auditory Scene Modification to Assist Hearing Impaired People" (Changheng Li, Giovanni Bologni, Jordi de Vries and Timm Bäumer).

13:30 - 13:45: Walk-in
13:45 - 14:30: Sharon Gannot (Bar-Ilan) - LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
14:35 - 14:55: Changheng Li (TUDelft)
14:55 - 15:15: Giovanni Bologni (TUDelft) 
Break
15:30 - 16:15: Zheng-Hua Tan (Aalborg University) - Self-supervised learning for speech and audio applications
16:20 - 16:40: Timm Bäumer (Oldenburg University) - Evaluation of an ITD-to-ILD transformation as a method to restore the spatial benefit in speech intelligibility in hearing impaired listeners
16:40 - 17:00: Jordi de Vries (TUDelft)
17: Drinks

PhD Thesis Defence

• Thursday, 18 September 2025
• 09:03-11:30
• Aula Senaatszaal

Multi-Microphone Signal Parameter Estimation in Various Acoustic Scenarios

Many modern devices, such as mobile phones, hearing aids and (hands-free) acoustic humanmachine interfaces are equipped with microphone arrays that can be used for various applications. These applications include source separation, audio quality enhancement, speech intelligibility improvement and source localization. In an ideal anechoic chamber, the signals received by ideal microphones are just attenuated and delayed version of the original sound. However, in practice, obstacles such as the floor, the ceiling and the surrounding walls will reflect the sound to the microphones. Also, the microphone itself will generate noise, distorting the recorded signals. Lastly, it is possible that multiple point sources are active simultaneously. When we consider one point source as the target signal, the other sources could be considered interfering signals. These distortions make it difficult to get access to the target signal. Therefore, spatial filtering is often applied to the microphone signals.

To achieve satisfying performance, these spatial filters typically need to be adaptive to the (changing) scene. Specifically, the filter coefficients depend on the acoustic-scene related parameters that model the microphone signals. These parameters, such as the relative transfer functions (RTFs) of the sources, the power spectral densities (PSDs) of the sources, the late reverberation and the ambient noise, are typically unknown in practice. Therefore, estimation of these parameters is crucial and thus the main focus of the dissertation. While it is relatively straightforward to estimate these parameters in less complex acoustic scenes, these algorithms are usually not applicable and not extendable to more complex acoustic scenes. Therefore, the complexity of the estimation methods needed depends on the complexity of the acoustic scene.

In his thesis, the author considers to estimate the RTF under varying assumptions and conditions, resulting in the joint estimation of the RTF and the power spectral densities of the sources, the late reverberation, and the noise.

PhD Thesis Defence

• Wednesday, 3 December 2025
• 17:30-19:00
• Aula Senaatszaal

Model-Based Processing in Ultrasound Imaging: Sparse Reconstruction and Coded Excitation

Ultrasound is a widely used real-time imaging modality to diagnose patients. Ultrasound imaging has several modes of operation such as ultrafast Doppler which, due to the high frame-rates, is particularly suited to image blood flow inside bodily organs such as the brain. Despite its success, the ultrafast imaging technique has some downsides such as lower overall signal-to-noise ratio (SNR), especially in deeper regions due to the use of unfocussed transmissions. This thesis explores the use of advanced signal processing methods such as model-based image reconstruction to regain some of the loss in SNR.

The first part of the thesis focus on advanced model-based image reconstruction techniques, incorporating complex priors or statistical assumptions about the signal and noise instead of using a simple physical propagation model. Conventional ultrasound beamforming techniques, such as the delay-and-sum (DAS) beamformer, perform well in many clinical settings; however, they face challenges in applications requiring high structural detail or SNR, such as vascular imaging. This thesis explores deterministic and statistical model-based vascular image reconstruction techniques to improve SNR, resolution, and clarity of fine vascular details. The proposed techniques exploit the joint sparsity of the vasculature images at different time instants. These methods enhance the depiction of vascular structures while increasing SNR and suppressing background noise and artifacts.

A large part of the thesis focuses on the sparse Bayesian learning (SBL) techniques. Starting with classical SBL, this thesis introduces the application of block-sparsity-based SBL techniques, such as pattern-coupled sparse Bayesian learning with fixed-point iterations and correlated sparse Bayesian learning. Although some of the proposed techniques are not computationally efficient yet for real-time ultrasound imaging, they do provide a new contribution to signal processing and computational imaging fields.

The final chapter of the thesis focuses on improving the ultrasound transmission to enhance the SNR. An optimized coded excitation technique has been proposed as an alternative to standard coded excitation techniques. By keeping the computational complexity to a modest level, the codes are optimized to increase the SNR without a significant loss in the image resolution. The Cramér-Rao lower bound (CRB) minimization and a faster alternative Fisher information matrix (FIM) maximization have been proposed to optimize the codes. The optimized codes are tested on simulated data to demonstrate their potential for flow imaging.

To sum up, this thesis contributes to the ultrasound blood flow imaging area through solutions on image reconstruction algorithms and ultrasound transmissions to overcome current limitations and challenges. This thesis explores using advanced modelbased signal processing methods to improve image quality. Therefore, this work contributes new strategies that can inspire future research and clinical applications in vascular ultrasound imaging.