scholarly journals Application of digital signal processing and machine learning for Electromyography: A review

2021 ◽  
Vol 1 (1) ◽  
pp. 30-45
Author(s):  
Siti Nashayu Omar
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Signal Analysis ◽  
Digital Signal ◽  
Signal Identification ◽  
Raw Data ◽  
Signal Characteristic ◽  
Emg Signal ◽  
The Common

This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

scholarly journals Introduction to automatic audio classification

2019 ◽  
Author(s):  
Tiago Tavares
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Classification System ◽  
Digital Signal ◽  
Audio Classification ◽  
Scientific Applications ◽  
Hands On

This hands-on workshop comprises essential techniques for digital signal processing and machine learning. Participants will use the Python libraries librosa and scikit-learn as support to build an automatic audio classification system. The workshop will use explorations in toy problems to approach theoretical aspects. Later, it will discuss practical issues for building a scientific applications in the field.

scholarly journals EASYDSP: PROBLEM-BASED LEARNING IN DIGITAL SIGNAL PROCESSING

2011 ◽  
Author(s):  
Kaveh Malakuti ◽  
Alexandra Branzan Albu
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Signal Analysis ◽  
Problem Based Learning ◽  
Digital Signal ◽  
Instructional Style ◽  
Time Problem ◽  
Course Format ◽  
The University ◽  
First Time

This paper presents a pilot pedagogical project that combined for the first time problem-based learning (PBL) with a lecture-based instructional style in Digital Signal Processing I. This new course format was offered during the Spring 2008 term at the University of Victoria, Canada. The PBL component was implemented via EasyDSP, a custom-designed framework for system design and signal analysis.

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