Accurate analysis of liver vasculature in three dimensions (3D) is essential for a variety of medical procedures including computer-aided diagnosis, treatment planning, or pre-operative planning of hepatic diseases.
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 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.
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.
This challenge addresses the global problem of hearing loss, which will affect 1 in 10 people by 2050. Hearing loss can create many issues with music: quieter passages being inaudible; poor and anomalous pitch perception; and difficulties identifying and picking out instruments and lyrics.
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.
A new Challenge on self-awareness in heterogeneous multi-robot systems has been organized within the first International Conference on Autonomous Systems (ICAS 2021). The research field of this competition is the unsupervised anomaly detection through self-aware autonomous systems, which is an active topic involving IEEE Signal Processing Society, also through the Autonomous Systems Initiative, and Intelligent Transportation Systems Society.
3D audio is gaining increasing interest in the machine learning community in recent years. The field of application is incredibly wide and ranges from virtual and real conferencing to game development, music production, autonomous driving, surveillance and many more. In this context, Ambisonics prevails among other 3D audio formats for its simplicity, effectiveness and flexibility.
The Clarity Project will be organising a series of machine learning challenges for advancing hearing-aid signal processing and speech-in-noise perception modelling.
In many real-world machine learning applications, AutoML is strongly needed due to the limited machine learning expertise of developers. Moreover, batches of data in many real-world applications may be arriving daily, weekly, monthly, or yearly, for instance, and the data distributions are changing relatively slowly over time. This presents a continuous learning or Lifelong Machine Learning challenge for an AutoML system.
In this competition, you are tasked with developing a controller to enable a physiologically-based human model with a prosthetic leg to walk and run. You are provided with a human musculoskeletal model, a physics-based simulation environment OpenSim where you can synthesize physically and physiologically accurate motion, and datasets of normal gait kinematics. You are scored based on how well your agent adapts to the requested velocity vector changing in real time.
In this competition you can take on the role of an attacker or a defender (or both). As a defender you are trying to build a visual object classifier that is as robust to image perturbations as possible. As an attacker, your task is to find the smallest possible image perturbations that will fool a classifier.
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.
In wireless communications, the pathloss (or large scale fading coefficient) quantifies the loss of signal strength between a transmitter (Tx) and a receiver (Rx) due to large scale effects, such as free-space propagation loss, and interactions of the radio waves with the obstacles (which block line-of sight, like buildings, vehicles, pedestrians), e.g. penetrations, reflections and diffractions.
Associated SPS Event: IEEE ICASSP 2022 Grand Challenge
Localizing the root cause of network faults is crucial to network operation and maintenance. Significant operational expenses will be saved if the root cause can be identified agilely and accurately. However, this is challenging for human beings due to the complicated wireless environments and network architectures.
Associated SPS Event: IEEE ICASSP 2021 Grand Challenge
In today’s digital age, network security is critical as billions of computers around the world are connected with each other over networks. Symantec’s Internet Security Threat Report indicates a 56% increase in the number of network attacks in 2019. Network anomaly detection (NAD) is an attempt to detect anomalous network traffic by observing traffic data over time to define what is “normal” traffic and pick out potentially anomalous behavior that differs in some way.
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.
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.
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.
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.
The Multimodal Information Based Speech Processing (MISP) 2022 Challenge aims to extend the application of signal processing technology in specific scenarios, using audio and video data. We target the home TV scenario, where 2-6 people communicate with each other with TV noise in the background. Our new tracks focus on audio-visual speaker diarization (AVSD), and audio-visual diarization and recognition (AVDR).
The MADReSS SPGC targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer’s Dementia (AD). Dementia is a category of neurodegenerative diseases that entails a long-term and usually gradual decrease of cognitive functioning.
This signal processing challenge is designed to get the latest advancements in speech enhancement applied to hearing aids. 430 million people worldwide require rehabilitation to address hearing loss. Yet even in developed countries, only 40% of people who could benefit from hearing aids have them and use them often enough, because they believe that hearing aids perform poorly.
The advent of spoken language processing (SLP) technologies on meeting transcripts is crucial for distilling, organizing, and prioritizing information. Meeting transcripts impose two key challenges to SLP tasks. First, meeting transcripts exhibit a wide variety of spoken language phenomena, leading to dramatic performance degradation. Second, meeting transcripts are usually long-form documents with several thousand words or more, posing a great challenge to mainstay Transformer-based models with high computational complexity.
The ICASSP 2023 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. For example, in the ICASSP 2022 Deep Noise Suppression challenge, the improvement in the background (BAK) and overall (OVRL) quality is impressive, but the improvement in the speech signal (SIG) is statistically zero.
The 5th DNS Challenge aims to motivate development of DNS models with great speech quality in presence of reverberation, noise and interfering (neighboring) talkers. DNS has gained momentum given new trends of hybrid and remote work in a variety of daily-life scenarios. Improving speech quality reduce meeting fatigue and improve clarity of communication.
Spoken Language Understanding (SLU) is a critical component of conversational voice assistants, requiring converting user utterances into a structured format for task executions. SLU systems typically consist of an ASR component to convert audio to text and an NLU component to convert text to a tree like structure, however recently, E2E SLU systems have also become of increasing interest in order to increase quality, model efficiency, and data efficiency.
The LIMMITS’23 challenge on LIghtweight, Multi-speaker, Multi-lingual Indic Text-to-Speech Synthesis is being organized as part of the Signal Processing Grand Challenge track at ICASSP 2023. As a part of this challenge, TTS corpora in Marathi, Hindi, and Telugu datasets will be released. These TTS corpora are being built in the SYSPIN project at SPIRE lab, Indian Institute of Science (IISc) Bangalore, India.
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
MISP Challenge 2021 has been accepted as a Signal Processing Grand Challenge (SPGC) of ICASSP 2022！Please refer to more details of ICASSP 2022 SPGC.
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
Recent development of speech signal processing, such as speech recognition, speaker diarization, etc., has inspired numerous applications of speech technologies. The meeting scenario is one of the most valuable and, at the same time, most challenging scenarios for speech technologies.
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.
The 5th CHiME Speech Separation and Recognition Challenge (CHiME-5). The new 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.
This 4th CHiME Speech Separation and Recognition Challenge (CHiME-4) challenge revisits the datasets originally recorded for CHiME-3, i.e., Wall Street Journal corpus sentences spoken by talkers situated in challenging noisy environments recorded using a 6-channel tablet based microphone array. CHiME-4 increases the level of difficulty by constraining the number of microphones available for testing.
The Multi-Genre Broadcast (MGB) Challenge is an evaluation of speech recognition, speaker diarization, dialect detection and lightly supervised alignment using TV recordings in English and Arabic. The speech data is broad and multi-genre, spanning the whole range of TV output, and represents a challenging task for speech technology. In 2015, the challenge used data from the British Broadcasting Corporation (BBC). It was an official challenge of the 2015 IEEE Automatic Speech Recognition and Understanding Workshop.
One year ago the 2011 PASCAL CHiME Speech Separation and Recognition Challenge considered the problem of recognising speech mixed in two-channel nonstationary noise typical of everyday listening conditions. Following the success of this challenge we are now organising a new challenge that, while keeping the same setting, extends the difficulty along two independent tracks: a larger vocabulary size and a more realistic mixing process that accounts for small head movements made while speaking.
In 2006 the PASCAL network funded the 1st Speech Separation challengewhich addressed the problem of separating and recognising speech mixed with speech. We are now launching a successor to this challenge that aims to tackle speech separation and recognition in more typical everyday listening conditions. The challenge employs noise background that has been collected from a real family living room using binaural microphones.
A new Challenge on self-awareness in heterogeneous multi-robot systems has been organized within the first International Conference on Autonomous Systems (ICAS 2021). The research field of this competition is the unsupervised anomaly detection through self-aware autonomous systems, which is an active topic involving IEEE Signal Processing Society, also through the Autonomous Systems Initiative, and Intelligent Transportation Systems Society.
The challenge aims at identifying novel signal processing solutions for discriminating between birds and drones appearing in video sequences. The specific goal is to detect a drone appearing at some time in a scene where birds may also be present, under different conditions. The algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. A dataset for training is made available upon request.
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.
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