I'm trying to understand how to use machine learning techniques for audio signal processing tasks like this: Is this possible at all? What kind of machine learning techniques would be worth exploring? The processing should work with different sample rates so I'm thinking the processing should not be done on the raw audio data but after some kind of a conversion step (DCT/FFT/wavelet/?).
In other words, the ability to take that glut of sensor data and make sense out of it requires a huge amount of processing on the platform, a process that enables users to sift through the signals on the receiving end and decide which countermeasures should follow. All of this requires acquisition and translation of real-time ISR imagery – or quickly and efficiently making sense of the data being recorded – a processing capability that calls for significant speed and power.
Also highlighted is, Nominations Open for 2020 SPS Awards, Call for Nominations: Awards Board and Nominations and Appointments Committee, Another Month of COVID-19, Interview with Chetan Arora, Associate Professor, IIT Delhi, India, Series to Highlight Young Professionals in Signal Processing: Dr. Thướng Nguyễn Cảnh, Series to Highlight Women in Signal Processing: Nita Patel, and much more! Finally, we'd like to invite graduate students and early-career researchers to submit brief descriptions of their research projects and/or interests to the newsletter. We would love to hear from you and will gladly disseminate it to our global audience. We also invite articles written in the area of Signal Processing or your area of research and expertise. We would also like to thank you all for your dedication and support to the Society!
The Imaging, Signals, and Machine Learning (ISML) group at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate with expertise in computer vision/image processing and data analytics. The ISML group conducts applied computer vision research and development addressing important issues of industrial and economic competitiveness, biomedical measurement science, and national security. The group consists of staff members with backgrounds in electrical engineering, computer science, and optical engineering, and frequently collaborates with partners in industry, academia, and other government organizations. To view the job description and apply, please visit bit.ly/ISML-Postdoc. ORNL is an equal opportunity employer.