scholarly journals Classifying cardiac rhythms by means of digital signal processing and machine learning

2020 ◽  
Vol 35 (1) ◽  
pp. 25-33
Author(s):  
Nielson Pinho ◽  
Diego Gomes ◽  
Adam Santos
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital 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.

Discrete signals: sampling, quantization and coding

2019 ◽  
pp. 265-298
Author(s):  
Max A. Little
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Real World ◽  
Digital Signal ◽  
Digital Data ◽  
Real Numbers ◽  
Representation Problem ◽  
Digital Storage ◽  
Discrete Signals

Digital signal processing and machine learning require digital data which can be processed by algorithms on computer. However, most of the real-world signals that we observe are real numbers, occurring at real time values. This means that it is impossible in practice to store these signals on a computer and we must find some approximate signal representation which is amenable to finite, digital storage. This chapter describes the main methods which are used in practice to solve this representation problem.

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.

Sign in / Sign up

Export Citation Format

Share Document