Data Challenges

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.

The Clarity Project will be organising a series of machine learning challenges for advancing hearing-aid signal processing and speech-in-noise perception modelling.

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.

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.

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.