Signal Processing Grand Challenge

Signal Processing Grand Challenges (SPGCs) are competitions driving advancements in various signal processing domains by focusing research efforts on specific, challenging problems. Current research emphasizes the development and application of deep learning models, including autoencoders and transformers, for tasks such as blind source separation, speech enhancement, and EEG decoding. These challenges address critical applications in areas like extended reality, healthcare (e.g., Alzheimer's detection and relapse prediction in psychotic patients), and human-computer interaction, fostering collaboration and pushing the boundaries of signal processing capabilities. The resulting improved algorithms and datasets significantly benefit both the scientific community and real-world applications.