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Data Challenges

(with available data challenges)
Current Challenges


Audio and Acoustic Signal Processing

2024

Supported by the SPS Challenge Program.

Personalised Head-Related Transfer Functions (HRTFs) have been shown to enhance auditory localization and immersion in mixed realities. However, relevant issues, such as the accurate acquisition of user-specific anatomical data, efficient simulation algorithms, and effective user validation, do not converge into a common and internationally recognized benchmark for evaluating HRTFs.

Someone with a hearing loss is listening to music via their hearing aids or headphones. The challenge is to develop a signal processing system that allows a personalised rebalancing of the music to improve the listening experience, for example by amplifying the vocals relative to the sound of the band. 

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.



Bio Imaging and Signal Processing

2024

Introducing ICASSP 2024 SPGC competition aiming at reconstructing skin spectral reflectance in the visible (VIS) and near-infrared (NIR) spectral range from RGB images captured by everyday cameras, offering a transformative approach for cosmetic and beauty applications. 

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). 

Various neuroimaging techniques can be used to investigate how the brain processes sound. Electroencephalography (EEG) is popular because it is relatively easy to conduct and has a high temporal resolution. Besides fundamental neuroscience research, EEG-based measures of auditory processing in the brain are also helpful in detecting or diagnosing potential hearing loss. 

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.



Image, Video, and Multidimensional Signal Processing

2024

Introducing ICASSP 2024 SPGC competition aiming at reconstructing skin spectral reflectance in the visible (VIS) and near-infrared (NIR) spectral range from RGB images captured by everyday cameras, offering a transformative approach for cosmetic and beauty applications. 

Speech-enabled systems often experience performance degradation in real-world scenarios, primarily due to adverse acoustic conditions and interactions among multiple speakers. Enhancing the front-end speech processing technology is vital for improving the performance of the back-end systems. 



Machine Learning for Signal Processing

2024

This challenge will require developing an engine for signal separation  of radio-frequency (RF) waveforms. At inference time, a superposition of a signal of interest (SOI) and an interfering signal will be fed to the engine, which should recover the SOI by performing a sophisticated interference cancellation. 

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.



Signal Processing for Communications and Networking

2024

This challenge will require developing an engine for signal separation  of radio-frequency (RF) waveforms. At inference time, a superposition of a signal of interest (SOI) and an interfering signal will be fed to the engine, which should recover the SOI by performing a sophisticated interference cancellation. 



Speech and Language Processing

2024

This challenge is the continuation of LIMMITS'23 (ICASSP 23 SPGC), it is aimed at making further progress in multi-speaker, multi-lingual TTS by extending the problem statement to voice cloning.  

The ICASSP 2024 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems.

Speech-enabled systems often experience performance degradation in real-world scenarios, primarily due to adverse acoustic conditions and interactions among multiple speakers. Enhancing the front-end speech processing technology is vital for improving the performance of the back-end systems. 

As cars become indispensable parts of human daily life, a safe and comfortable driving environment is more desirable. The traditional touch-based interaction in cockpit is easy to distract the drivers' attention, leading to inefficient operations and potential security risks. 

Past Challenges


Audio and Acoustic Signal Processing

2023

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.

The L3DAS23 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with a particular focus on 3D speech enhancement (SE) and 3D sound event localization and detection (SELD) in augmented reality applications.

Verbal communication in noisy environments can be hard. Speech enhancement using head-worn microphone arrays, such as hearing aids or augmented reality devices offers the opportunity to make it easier. However, the highly dynamic nature of the listening situation presents some challenges.

2022

Associated SPS Event: IEEE ICASSP 2022 Grand Challenge

Over the last few years, the technology of speech synthesis and voice conversion has made significant improvement with the development of deep learning. The models can generate realistic and human-like speech. It is difficult for most people to distinguish the generated audio from the real. However, this technology also poses a great threat to the global political economy and social stability if some attackers and criminals misuse it with the intent to cause harm. 

Associated SPS Event: IEEE ICASSP 2022 Grand Challenge

Noise suppression has become more important than ever before due to the increasing use of voice interfaces for various applications. Given the millions of internet-connected devices being employed for audio/video calls, noise suppression is expected to be effective for all noise types chosen from daily-life scenarios.

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.

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 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. 

2021

Associated SPS Event: IEEE ICASSP 2021 Grand Challenge

The ICASSP 2021 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. Many recent AEC studies report good performance on synthetic datasets where the train and test samples come from the same underlying distribution.

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.

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.

2019

The Interspeech 2019 Computational Paralinguistics ChallengE (ComParE) is an open Challenge dealing with states and traits of speakers as manifested in their speech signal’s properties.

DIHARD II is the second in a series of diarization challenges focusing on "hard" diarization; that is, speaker diarization for challenging recordings where there is an expectation that the current state-of-the-art will fare poorly.

2018

The IEEE AASP Challenge on acoustic source LOCalization And TrAcking (LOCATA) aims at providing researchers in source localization and tracking with a framework to objectively benchmark results against competing algorithms using a common, publicly released data corpus that encompasses a range of realistic scenarios in an enclosed acoustic environment. Data corresponding to the LOCATA challenge

2017

The IEEE Signal Processing Society announced the fourth edition of the Signal Processing Cup: a real-time beat tracking challenge. The beat is a salient periodicity in a music signal. It provides a fundamental unit of time and foundation for the temporal structure of the music. As Meinard Müller says (Fundamentals of Music Processing, Springer, 2015), “It is the beat that drives music forward and provides the temporal framework of a piece of music. Intuitively, the beat corresponds to the pulse a human taps along when listening to music.”

2016

The workshop aims to provide a venue for researchers working on computational analysis of sound events and scene analysis to present and discuss their results. We aim to bring together researchers from many different universities and companies with interest in the topic, and provide the opportunity for scientific exchange of ideas and opinions. The workshop is organized as a satellite event to the 2016 European Signal Processing Conference (EUSIPCO).

2014

The ACE Challenge was part of the programme of Challenges organised by the IEEE Audio and Acoustic Signal Processing Technical Committee. The aim of this challenge was to evaluate state-of-the-art algorithms for blind acoustic parameter estimation from speech and to promote the emerging area of research in this field. Participants will evaluate their algorithms for T60 and DRR estimation against the ‘ground truth’ values provided with the data-sets. Furthermore, they are expected to present the results in a paper describing the method used.

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 challenge will consider the problem of distant multi-microphone conversational speech recognition in everyday home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech.

2013

The workshop aims to provide a venue for researchers working on computational analysis of sound events and scene analysis to present and discuss their results. We aim to bring together researchers from many different universities and companies with interest in the topic, and provide the opportunity for scientific exchange of ideas and opinions. 



Bio Imaging and Signal Processing

2023

The challenge will concern the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors embedded in smartwatches, in order to extract high-level representations of the wearer’s activity and behavior for two downstream tasks: 1) Identification of the wearer of the smartwatch, and 2) Detection of relapses in 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.

Various neuroimaging techniques can be used to investigate how the brain processes sound. Electroencephalography (EEG) is popular because it is relatively easy to conduct and has a high temporal resolution. An increasingly popular method in these fields is to relate a person’s electroencephalogram (EEG) to a feature of the natural speech signal they were listening to. This is typically done using linear regression or relatively simple neural networks to predict the EEG signal from the stimulus or to decode the stimulus from the EEG.

2022

Associated SPS Event: IEEE ICIP 2022 Grand Challenge

Intestinal parasitic infections remain among the leading causes of morbidity worldwide, especially in tropical and sub-tropical areas with more temperate climates. According to WHO, approximately 1.5 billion people, or 24% of the world’s population, are infected with soil-transmitted helminth infections (STH), and 836 million children worldwide required preventive chemotherapy for STH in 2020.

2021

Associated SPS Event: IEEE ICASSP 2021 Grand Challenge

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries around the world affecting millions and claiming more than 1.5 million human lives, since its first emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems.

2020

Translational utility is the ability of certain biomedical imaging features to capture useful subject-level characteristics in clinical settings, yielding sensible descriptions and/or predictions for individualized treatment trajectory. An important step in achieving translational utility is to demonstrate the association between imaging features and individual characteristics, such as sex, age, and other relevant assessments, on a large out-of-sample unaffected population (no diagnosed illnesses). This initial step then provides a strong normative basis for comparison with patient populations in clinical settings. Detailed information. Website.

 

 

2019

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.

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 2012, Cell Tracking Challenge (CTC) was launched to objectively compare and evaluate state-of-the-art whole-cell and nucleus segmentation and tracking methods using both real (2D and 3D) time-lapse microscopy videos of cells and nuclei, along with computer generated (2D and 3D) video sequences simulating nuclei moving in realistic environments. To address numerous requests for benchmarking only cell segmentation methods (without tracking), we are launching now a new time-lapse cell segmentation benchmark on the same datasets (plus one new dataset).

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.

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.

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).

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.

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.

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