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.

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 2022 Grand Challenge

The L3DAS22 Challenge aims at encouraging and fostering research on machine learning for 3D audio signal processing. 3D audio is gaining increasing interest in the machine learning community in recent years. The range of applications is incredibly wide, extending from virtual and real conferencing to autonomous driving, surveillance and many more.

Associated SPS Event: IEEE ICASSP 2021 Grand Challenge

Text-to-speech (TTS) or speech synthesis has witnessed significant performance improvement with the help of deep learning. The latest advances in end-to-end text-to-speech paradigm and neural vocoder have enabled us to produce very realistic and natural-sounding synthetic speech reaching almost human-parity performance. But this amazing ability is still limited to the ideal scenarios with a large single-speaker less-expressive training set.

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 proposed challenge seeks to push the limits of deep learning algorithms for 3D cone beam computed tomography (CBCT) reconstruction from low-dose projection data (sinogram). 

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.

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.

Computer assisted tools can provide cost effective and easily deployable solutions for cancer diagnostics. The aim of this challenge is to build a classifier for the identification of leukemic versus normal immature cells for while blood cancer, namely, B-ALL diagnostics. A dataset of cells with class labels, marked by the expert based on the domain knowledge, will be provided at the subject-level to train the classifier. This problem is interesting because the two cell types appear similar under the microscope and subject-level variability plays a key role.

In digital pathology, it is often useful to align spatially close but differently stained tissue sections in order to obtain the combined information. The images are large, in general, their appearance and their local structure are different, and they are related through a nonlinear transformation. The proposed challenge focuses on comparing the accuracy and approximative speed of automatic non-linear registration methods for this task. Registration accuracy will be evaluated using manually annotated landmarks.

CHAOS has two separate but related aims:

1. Segmentation of liver from computed tomography (CT) data sets, which are acquired at portal phase after contrast agent injection for pre-evaluation of living donated liver transplantation donors (15 training + 15 test sets).
2. Segmentation of four abdominal organs (i.e. liver, spleen, right and left kidneys) from magnetic resonance imaging (MRI) data sets acquired with two different sequences (T1-DUAL and T2-SPIR) (15 training + 15 test sets).

BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas.

Endoscopic Artefact Detection (EAD) is a core challenge in facilitating diagnosis and treatment of diseases in hollow organs. Precise detection of specific artefacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realising reliable computer-assisted tools for improved patient care.

Diffusion MRI has emerged as a key modality for imaging brain tissue microstructural features, yet, validation is necessary for accurate and useful biomarkers. Towards this end, we present the two-year ISBI 2019/2020 diffusion Mri whitE Matter rEcoNstrucTiOn (MEMENTO) challenge. The first year is dedicated to designing the challenge, building the appropriate dataset(s), and making it available to the community. The challenge and participant submissions will take place in the second year, with the aim to evaluate and advance the state of the microstructural modeling field.