IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.

Speech recognition technology allows computers to take spoken audio, interpret it and generate text from it. But how do computers understand human speech? The short answer is…the wonder of signal processing. 

Alan Bovik has a storied career. An American engineer, vision scientist and Primetime Emmy Award winner, Bovik holds the Cockrell Family Endowed Regents Chair in Engineering at The University of Texas at Austin, where he has been the Director of the Laboratory for Image and Video Engineering for more than three decades.

The list of tables of contents (TOCs) are now available for December 2018. The TOCs are formatted to the style of its publication and offers not only links to the full issue, but also to the individual articles in IEEEXplore®.

Five colleagues were honored with the appointment of Distinguished Lecturer and they are:  Petros Boufounos (Mitsubishi Electric Research Laboratories), Israel Cohen (Technion - Israel Institute of Technology), Janusz Konrad (Boston University), Anna Scaglione (Arizona State University), and Rui Zhang (National University of Singapore).

In patent no. 9,477,869 a mutual-capacitance palm print identification method is provided, a mutual-capacitance palm print identification device and a mutual-capacitance palm print identification touch panel. 

This study investigates various aspects of multi-speaker interference and its impact on speaker recognition. Single-channel multi-speaker speech signals (aka co-channel speech) comprise a significant portion of speech processing data. Examples of co-channel signals are recordings from multiple speakers in meetings, conversations, debates, etc.

Improving the modeling and processing of nonstationary signals remains an important yet challenging problem. In the past, the most effective approach for processing these signals has been statistical modeling.

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things (IoT). As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by system architects and design engineers, and present a new set of design trade-offs.

The IEEE Signal Processing Society Malaysia Chapter has been selected as the recipient of the 2018 Chapter of the Year Award!