As an authentication method, biometrics offer unparalleled convenience and security. With very little for users to remember and do, there is also very little that they can do incorrectly, thus limiting the attack surface of an authentication system. Unfortunately, biometrics also present a challenging privacy/security tradeoff: biometric data...

IEEE Fellow is the highest grade of membership of the IEEE. It honors members with an outstanding record of technical achievements, contributing importantly to the advancement or application of engineering, science and technology, and bringing significant value to society.

The IEEE Signal Processing Society invites nominations for the position of Editor-in-Chief for the following journals: IEEE Signal Processing Magazine, IEEE Transactions on Image Processing, and IEEE Transactions on Signal Processing for a 3-year term starting 1 January 2024.  

Self-awareness and self-organization have a long history in biology, psychology, medicine, engineering and (more recently) computing. In the past decade this has inspired new self-aware/self-organizing strategies for building resilient computing platforms that can adapt to the (often conflicting) challenges...

Augmented reality devices of the future will likely fuse sensor data from several modalities, allowing multichannel speech enhancement algorithms to exploit, for example, head orientation and accurately estimated source directions. 

Nominations are due 15 January annually for the IEEE Technical Field Awards (TFAs). Download nomination forms and award-specific criteria.

Three new Members-at-Large will take their seats on the IEEE Signal Processing Society Board of Governors beginning 1 January 2023 and will serve until 31 December 2025. Seven candidates competed for the three Member-at-Large positions. 

Separating the household aggregated energy consumption signal into its additive sub-components (the power signal from individual appliances), is called energy (power) disaggregation or non-intrusive load monitoring (NILM).

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources.

As the most widely-used spatial filtering approach for multi-channel signal separation, beamforming extracts the target signal arriving from a specific direction. We present an emerging approach based on multi-channel complex spectral mapping, which trains a deep neural network (DNN) to directly estimate the real and imaginary spectrograms of the target signal from those of the multi-channel noisy mixture.