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.

Original languageEnglish (US)
Title of host publicationFrontiers in Education
Subtitle of host publicationFostering Innovation Through Diversity, FIE 2018 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538611739
StatePublished - Jul 2 2018
Event48th Frontiers in Education Conference, FIE 2018 - San Jose, United States
Duration: Oct 3 2018Oct 6 2018

Publication series

NameProceedings - Frontiers in Education Conference, FIE
ISSN (Print)1539-4565


Conference48th Frontiers in Education Conference, FIE 2018
Country/TerritoryUnited States
CitySan Jose


  • Linear Predictive Coding
  • Machine Learning
  • Online labs
  • Speech recognition

ASJC Scopus subject areas

  • Software
  • Education
  • Computer Science Applications


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