Machine Learning for Signal Processing

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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.

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.

Past Members

Technical Committee

Past Members

 

The following lists all the past chairs and members of the SPS Machine Learning for Signal Processing Technical Committee. 

*NOTE: Please scroll up/down and left/right within the Past Members window to view the full list. Also, view the full downloadable list (right-click, Save As to save file).

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

EDICS

Technical Committee

EDICS

NOTE: The Technical Committee's EDICS list is derived from the Society's Unified EDICS list. You can view the Society's complete Unified EDICS ist and EDICS list approval process on the Unified EDICS page.

 

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 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 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.

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