The Signal, Image Processing, and Pattern Recognition Group (SIPPRE) was established in 2020 by Dr. Athanasios Koutras, Associate Professor at the ECE Department of the University of Peloponnese. Despite its recent foundation, SIPPRE has brought together a diverse and talented group of academics from universities and research centers, along with Postdoctoral Researchers, PhD candidates, and graduate students. This collaborative environment fosters high-quality research in digital signal processing and pattern recognition, addressing complex challenges across various domains.

The group’s research themes span three primary areas:

A. Audio Processing and Analysis

Speech Processing
Developing advanced techniques for analyzing and enhancing speech signals, focusing on applications such as noise reduction, speech enhancement, and robust communication systems.

Speech Recognition
Designing systems that accurately recognize and transcribe spoken language, with applications in virtual assistants, transcription tools, and accessibility technologies.

Blind Source Separation
Innovating algorithms to isolate individual audio sources from mixed recordings, with applications in audio restoration, multi-speaker scenarios, and surveillance.

Music Information Retrieval
Extracting meaningful information from music, such as melody extraction, genre classification, and automatic tagging, to support diverse applications in musicology and entertainment.

Emotion Recognition from Speech and Music Signals
Investigating how speech and music convey emotions to develop systems for affective computing, therapeutic tools, and personalized user experiences.

B. Biomedical Signal Processing and Analysis

EEG Brain Signal Analysis
Creating methods to process and interpret electroencephalogram (EEG) signals, offering insights into neural health, cognitive functions, and brain activity.

Sleep Study of Brain Functionality Using EEG
Analyzing sleep-related EEG signals to understand brain functionality and diagnose sleep disorders, contributing to better health outcomes.

Brain-Computer Interface (BCI) Applications
Designing systems that enable individuals to control devices directly through brain signals, with applications in assistive technology, gaming, and rehabilitation.

BCI Applications in Entertainment
Enhancing interactive experiences by integrating brain-computer interfaces into gaming, virtual reality, and creative arts, making entertainment more immersive and personalized.

Emotion Recognition Using EEG Signal Analysis
Utilizing EEG signals to identify emotional states, paving the way for advancements in mental health monitoring, affective computing, and adaptive systems.

C. Medical Image Analysis

Detection of Cancer in Digital Mammograms Using GANs
Leveraging Generative Adversarial Networks (GANs) to transform and analyze mammograms, enabling the detection of cancerous regions with enhanced precision and accuracy.

Recognition of Abnormalities in Breast Imaging
Developing tools for identifying and classifying abnormalities in a range of breast imaging modalities, including digital mammography, digital breast tomosynthesis, and MRI, to support comprehensive diagnostics.

Computer-Assisted Diagnosis (CAD) System Design
Designing intelligent systems to aid radiologists in interpreting medical images, streamlining diagnostic workflows and improving outcomes for patients.

Breast Cancer Detection Using Histopathology Biopsy Images
Creating advanced algorithms for analyzing histopathology biopsy images to detect breast cancer, integrating insights from tissue-level imaging to complement non-invasive diagnostic methods.

SIPPRE’s research portfolio reflects its commitment to advancing innovative solutions in signal and image processing, addressing real-world challenges in healthcare, entertainment, and beyond.