Name: Danielle Yaffe Degree: Electrical Engineering, Audio Signal Processing Subject: “Audio-Visual Speech Enhancement for Spatial Audio” Name: Tslil Sardam Degree: M.Sc. in Electrical & Computer Engineering (with Thesis) Seminar Topic: State Estimation with Measurement Sign Errors

Name: Danielle Yaffe Degree: Electrical Engineering, Audio Signal Processing Professor: Prof. Boaz Rafaely Subject: “Audio-Visual Speech Enhancement for Spatial Audio” Abstract: Audio-visual speech enhancement (AVSE) has been found to be particularly useful at low signal-to-noise (SNR) ratios due to the immunity of the visual features to acoustic noise. However, a significant gap exists in AVSE methods tailored to enhance spatial audio under low-SNR conditions. The latter is of growing interest with augmented reality applications. To address this gap, we present a multi-channel AVSE framework based on VisualVoice that leverages spatial cues from microphone arrays and visual information for enhancing the target speaker in noisy environments. We also introduce MAVe, a novel database containing multi-channel audio-visual signals in controlled, reproducible room conditions across a wide range of SNR levels. Experiments demonstrate that the proposed method consistently achieves significant gains in SI-SDR, STOI, and PESQ, particularly in low SNRs. Binaural signal analysis further confirms the preservation of spatial cues and intelligibility. Abstract: Accurate state estimation is central to reliable power system operation. Classical weighted least-squares methods perform well under nominal conditions but are vulnerable to certain measurement anomalies. In particular, measurement sign errors can arise from hardware faults, communication issues, or malicious interference, and may reverse the physical meaning of measurements while remaining difficult to detect using conventional techniques. In this seminar, we present an approach that explicitly accounts for sign errors in power system measurements. By jointly estimating the system state and the underlying measurement signs, the proposed method enables the detection and correction of sign-flipped data that standard methods often miss. Simulation results on benchmark power systems demonstrate improved estimation accuracy and enhanced anomaly detection compared to traditional state estimation approaches. Name: Tslil Sardam Supervisors: Prof. Tirza Routtenberg and Prof. Eran Treister Degree: M.Sc. in Electrical & Computer Engineering (with Thesis) Seminar Topic: State Estimation with Measurement Sign Errors Abstract: Accurate state estimation is central to reliable power system operation. Classical weighted least-squares methods perform well under nominal conditions but are vulnerable to certain measurement anomalies. In particular, measurement sign errors can arise from hardware faults, communication issues, or malicious interference, and may reverse the physical meaning of measurements while remaining difficult to detect using conventional techniques. In this seminar, we present an approach that explicitly accounts for sign errors in power system measurements. By jointly estimating the system state and the underlying measurement signs, the proposed method enables the detection and correction of sign-flipped data that standard methods often miss. Simulation results on benchmark power systems demonstrate improved estimation accuracy and enhanced anomaly detection compared to traditional state estimation approaches