scholarly journals Emerging Trends in Machine Learning for Signal Processing

2017 ◽  
Vol 2017 ◽  
pp. 1-2 ◽  
Author(s):  
George A. Papakostas ◽  
Konstantinos I. Diamantaras ◽  
Francesco A. N. Palmieri
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Emerging Trends

Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition

2021 ◽  
Vol 68 ◽  
pp. 102577
Author(s):  
Yang Zhou ◽  
Chaoyang Chen ◽  
Mark Cheng ◽  
Yousef Alshahrani ◽  
Sreten Franovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Methods ◽  
Semg Signal ◽  
Motion Recognition ◽  
Machine Learning Methods ◽  
Shoulder Motion

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

scholarly journals An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hayssam Dahrouj ◽  
Rawan Alghamdi ◽  
Hibatallah Alwazani ◽  
Sarah Bahanshal ◽  
Alaa Alameer Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Optimization Problems

Bayesian Machine Learning: EEG\/MEG signal processing measurements

2016 ◽  
Vol 33 (1) ◽  
pp. 14-36 ◽  
Author(s):  
Wei Wu ◽  
Srikantan Nagarajan ◽  
Zhe Chen
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Bayesian Machine Learning

Machine Learning in Signal Processing

2021 ◽  
Author(s):  
Sudeep Tanwar ◽  
Anand Nayyar ◽  
Rudra Rameshwar
Keyword(s):  
Machine Learning ◽  
Signal Processing
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