IEEE Signal Processing Society Past President Ahmed Tewfik, in his capacity as Chair of the Society’s Nominations and Appointments Committee, invites nominations for the IEEE Signal Processing Society Officer positions of President-Elect for the term 1 January 2024-31 December 2025, Vice President-Conferences for the term of 1 January 2024-31 December 2026 and Vice President-Publications for the term of 1 January 2024-31 December 2026.

IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.

The Signal Processing Society (SPS) has 12 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.

Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. In this blog, we introduce pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature.

Recent years, face recognition has made a remarkable breakthrough due to the emergence of deep learning. However, compared with frontal face recognition, many deep face recognition models still suffer serious performance degradation when handling profile faces. To address this issue, we propose a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition. Most existing discriminative feature learning approaches project features from the same class into a separated latent subspace.

Graph neural networks (GNNs) have achieved impressive results in various graph learning tasks and they have found their way into many application domains. Despite their proliferation, our understanding of their robustness properties is still very limited. 

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this work explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. 

Recent advances in multimodal processing have led to promising solutions for speech-processing tasks. One example is automatic speech recognition (ASR), which is a key component in current speech-based systems.

IEEE Signal Processing Society Past President Ahmed Tewfik, in his capacity as Chair of the Society’s Nominations and Appointments Committee, invites nominations for the IEEE Signal Processing Society Officer positions of President-Elect, Vice President-Conferences, and Vice President-Publications.

The IEEE SPS congratulates the following SPS members who will receive the Society’s prestigious awards during ICASSP 2023 in Greece.