Emerging Machine Learning Techniques in Signal Processing

Emerging Machine Learning Techniques in Signal Processing

In the era of knowledge-based society and machine automation , there is a strong interest in machine learning (ML) techniques in a wide range of applications. The attention paid to ML methods within the DSP community is not new. Speech recognition is an example of an area where DSP and machine learning have been combined to develop efficient and robust speech recognizers. Channel equalization i...

Emerging Trends in Machine Learning for Signal Processing

Machine Learning for Signal Processing

The 25th MLSP workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions. Prospective authors are invited to submit papers on relevant algorithms and applications incl...

Trends in Machine Learning for Signal Processing

SP AND ERROR CORRECTION FOR NONVOLATILE MEMORY DEVICES Nonvolatile storage devices in the form of NAND flash memories and solidstate drives have become the storage techniques of choice in many mobile and portable devices. The continued density growth of these devices has been mainly driven by aggressive technology scaling and the use of multilevel per-cell techniques. However, bit errors are be...

Extreme Learning Machine for Graph Signal Processing

In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such...