IEEE, the world's largest technical professional organization dedicated to advancing technology for humanity, and the IEEE Signal Processing Society (SPS) will hold the 2024 IEEE International Conference on Image Processing (ICIP 2024), 27-30 October 2024 at the Abu Dhabi National Exhibition Centre in Abu Dhabi, UAE. ICIP 2024 will be a hub of innovation and learning - examining how AI-based approaches in the field are bringing opportunities, as well as challenges.

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.

Earlier this year, the Signal Processing Society Board of Governors approved the formation of the SPS Industry Board. The Industry Board reports directly to the Board of Governors, ensuring that an industry perspective is reflected in all society operations, including technical directions, conferences, publications, membership, education, and awards. 

Fernando Pereira is a Full Professor at the Department of Electrical and Computers Engineering of Instituto Superior Técnico, University of Lisbon, and a Senior Researcher at Instituto de Telecomunicações, Lisbon, Portugal. 

To address rapidly growing interest in artificial intelligence (AI) and machine learning (ML) for signal processing and data science, the IEEE Signal Processing Society (SPS) has launched a new special series on AI in Signal & Data Science, to be published within the IEEE Journal on Selected Topic in Signal Processing (JSTSP).

How can we process and remix music so it sounds best for those with a hearing loss? The Cadenza project is defining what music personalised for someone with a hearing loss should sound like and exploiting the latest in machine learning to create improved listening experiences.

Inspired by the capabilities of transformer models, we introduce a novel method named Multivariate Time-Series Imputation with Transformers (MTSIT). This entails an unsupervised autoencoder model featuring a transformer encoder, leveraging unlabeled observed data for simultaneous reconstruction and imputation of multivariate time-series.