TY - GEN
T1 - Online Machine Learning Experiments in HTML5
AU - Dixit, Abhinav
AU - Shanthamallu, Uday Shankar
AU - Spanias, Andreas
AU - Berisha, Visar
AU - Banavar, Mahesh
N1 - Funding Information:
VIII. ACKNOWLEGEMENTS The work at Arizona State University is supported in part by the NSF DUE award 1525716 and the SenSIP Center. The work at Clarkson University is supported in part by the NSF DUE award 15255224.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This work in progress paper describes software that enables online machine learning experiments in an undergraduate DSP course. This software operates in HTML5 and embeds several digital signal processing functions. The software can process natural signals such as speech and can extract various features, for machine learning applications. For example in the case of speech processing, LPC coefficients and formant frequencies can be computed. In this paper, we present speech processing, feature extraction and clustering of features using the K-means machine learning algorithm. The primary objective is to provide a machine learning experience to undergraduate students. The functions and simulations described provide a user-friendly visualization of phoneme recognition tasks. These tasks make use of the Levinson-Durbin linear prediction and the K-means machine learning algorithms. The exercise was assigned as a class project in our undergraduate DSP class. The description of the exercise along with assessment results is described.
AB - This work in progress paper describes software that enables online machine learning experiments in an undergraduate DSP course. This software operates in HTML5 and embeds several digital signal processing functions. The software can process natural signals such as speech and can extract various features, for machine learning applications. For example in the case of speech processing, LPC coefficients and formant frequencies can be computed. In this paper, we present speech processing, feature extraction and clustering of features using the K-means machine learning algorithm. The primary objective is to provide a machine learning experience to undergraduate students. The functions and simulations described provide a user-friendly visualization of phoneme recognition tasks. These tasks make use of the Levinson-Durbin linear prediction and the K-means machine learning algorithms. The exercise was assigned as a class project in our undergraduate DSP class. The description of the exercise along with assessment results is described.
KW - Linear Predictive Coding
KW - Machine Learning
KW - Online labs
KW - Speech recognition
UR - http://www.scopus.com/inward/record.url?scp=85063513851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063513851&partnerID=8YFLogxK
U2 - 10.1109/FIE.2018.8659113
DO - 10.1109/FIE.2018.8659113
M3 - Conference contribution
AN - SCOPUS:85063513851
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - Frontiers in Education
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th Frontiers in Education Conference, FIE 2018
Y2 - 3 October 2018 through 6 October 2018
ER -