SiPML 2020


The 6th International Workshop on Signal Processing and Machine Learning

October 28-30, 2020, Yonago, Tottori, Japan 

In Conjunction with 15th 3PGCIC-2020 Conference                   

Because of COVID-19, we are considering both cases: physical conference or virtual conference (online-presentations). We do hope that the situation will improve until the end of October and we have physical conference. Even in the worst case that the situation will not improve, based on the contract we have with Springer, we will publish the accepted papers in Springer Book Series on Lecture Notes in Networks and Systems and make online presentations.


The workshop will bring together engineers, students, practitioners, and researchers from the fields of machine learning (ML) and signal processing (SP).  The aim of the workshop is to contribute to the cross-fertilization between the research on ML methods and their application to SP to initiate collaboration between these areas. ML usually plays an important role in the transition from data storage to decision systems based on large  databases of signals such as the obtained from sensor networks, internet services, or communication systems. These systems imply developing both computational solutions and novel models. Signals from real-world systems are usually complex such as speech, music, bio-medical, multimedia, among others. Thus, SP techniques are very useful for these type of systems to automate processing and analysis techniques to retrieve information from data storage. Topics will range from foundations for real-world systems, and processing, such as speech, language analysis, biomedicine, convergence and complexity analysis, machine learning, social networks, sparse representations, visual analytics, robust statistical methods.



Learning theory
Cognitive information processing
Neural networks
Classification and pattern recognition
Nonlinear signal processing
Graphical models and kernel methods
•Genomic signals and  sequences
Multichannel adaptive signal processing
Kernel methods and graphical models
Sparsity-aware learning
Subspace/maniforld learning
Bayesian and distributed learning
Smart Grid, games, social networks
Computational Intelligence
Data-driven adaptive systems
Data-driven models
Multimodal data fusion
Multiset data analysis
Perceptual signal processing
Applications (biomedical signals, biometrix, bioinformatics)