SPS Newsletter Article

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.

Voice conversion (VC) is a significant aspect of artificial intelligence. It is the study of how to convert one’s voice to sound like that of another without changing the linguistic content.

Crowdsourcing has emerged as a powerful paradigm for tackling various machine learning, data mining, and data science tasks, by enlisting inexpensive crowds of human workers, or annotators, to accomplish learning and inference tasks.

Thanks to their ability to monitor physical activity and health-related parameters, wearable devices are becoming nowadays more and more popular. In addition to what they already offer, an interesting capability achievable through such devices is biometric recognition.

Over the past twenty years, functional connectivity of the human brain has been studied using tools from complex network theory. One such tool is community detection which  is  fundamental for uncovering the links between structure and function in complex networks.

The IEEE Signal Processing Society (SPS) is happy to announce that Prof. Yonggang Wen will be the new Editor-in-Chief (EIC) of the IEEE Transactions on Multimedia (T-MM). His three-year term will be from January 2023 to December 2025.

Intelligent reflecting surface (IRS) has recently emerged as a promising paradigm for future wireless communications. Owing to its remarkable capability in reshaping the propagation environment, IRS has a great potential to boost spectral efficiency, mitigate interference, and enhance physical security. 

With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin.

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. 

With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin.