Data Challenges

PLC is an important part of audio telecommunications technology and codec development, and methods for performing PLC using machine learning approaches are now becoming viable for practical use.

The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic latency to 20ms, and including a full-band version of AECMOS.

Associated SPS Event: IEEE ICASSP 2022 Grand Challenge

The ICASSP 2022 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. 

Listening in noisy reverberant environments can be challenging. The recent emergence of hearable devices, such as smart headphones, smart glasses and virtual/augmented reality headsets, presents an opportunity for a new class of speech and acoustic signal processing algorithms which use multimodal sensor data to compensate for, or even exploit, changes in head orientation. 

Associated SPS Event: IEEE ICASSP 2021 Grand Challenge

The ICASSP 2021 Deep Noise Suppression (DNS) challenge is designed to foster innovation in the field of noise suppression to achieve superior perceptual speech quality. We recently organized a DNS challenge special session at INTERSPEECH 2020. We open sourced training and test datasets for researchers to train their noise suppression models. We also open sourced a subjective evaluation framework and used the tool to evaluate and pick the final winners. Many researchers from academia and industry made significant contributions to push the field forward.

Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multi-channel de-reverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge that provides an opportunity to the researchers in the field to carry out a comprehensive evaluation of their methods based on a common database and on common evaluation metrics.

The  2nd e-Prevention challenge (https://robotics.ntua.gr/icassp2024-eprevention-spgc/) aims to stimulate innovative research on the prediction and identification of mental health relapses via the analysis and processing of the digital phenotype of patients in the psychotic spectrum.

Supported by the SPS Challenge Program

The George B. Moody PhysioNet Challenges are annual competitions that invite participants to develop automated approaches for addressing important physiological and clinical problems. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts. 

Epilepsy is one of the most common neurological disorders, affecting almost 1% of the population worldwide. The categorization of seizures is usually made based on the seizure onset zone (area of the brain where the seizure initiates) the progression of the seizure and the awareness status of the patient that experience the seizure. Focal onset seizures are the most common type of seizures in adults with epilepsy.

Digital pathology has been gradually introduced in clinical practice. Although the digital pathology scanner could give very high resolution whole-slide images (WSI) (up to 160nm per pixel), the manual analysis of WSI is still a time-consuming task for the pathologists. Automatic analysis algorithms offer a way to reduce the burden for pathologists. Our proposed challenge will focus on automatic detection and classification of lung cancer using Whole-slide Histopathology. This subject is highly clinical relevant because lung cancer is the top cause of cancer-related death in the world.

The goal of the challenge is to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). There are two tasks and therefore two leaderboards for evaluating the performance of the algorithms. Participants can choose to join both or either tasks according to their interests.

Skin cancer is the most common cancer globally, with melanoma being the most deadly form. Dermoscopy is a skin imaging modality that has demonstrated improvement for diagnosis of skin cancer compared to unaided visual inspection. However, clinicians should receive adequate training for those improvements to be realized.

This challenge aims at creating an open and fair competition for various research groups to test and validate their methods, particularly for the multi-sequence ventricle and myocardium segmentation.

The aim of this challenge is to learn effective machine learning models that can estimate a set of clinical significant LV indices (regional wall thicknesses, cavity dimensions, area of cavity and myocardium, cardiac phase) directly from MR images. No intermediate segmentation is required in the whole procedure.

The aim is to provide a formal framework for evaluating the current state of the art, gather researchers in the field and provide high quality data with protocols for validating endoscopic vision algorithms.