Probabilistic segmentation of time-series audio signals using Support Vector Machines

Haik Kalantarian*, Bobak Mortazavi, Mohammad Pourhomayoun, Nabil Alshurafa, Majid Sarrafzadeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


To allow health tracking, patient monitoring, and provide timely user interventions, sensor signals from body sensor networks need to be processed in real-time. Time subdivisions of the sensor signals are extracted and fed into a supervised learning algorithm, such as Support Vector Machines (SVM), to learn a model capable of distinguishing different class labels. However, selecting a short-duration window from the continuous data stream is a significant challenge, and the window may not be properly centered around the activity of interest. In this work, we address the issue of window selection from a continuous data stream, using an optimized SVM-based probability model. To evaluate the effectiveness of our approach, we apply our algorithm to audio signals acquired from a wearable nutrition-monitoring necklace. Our optimized algorithm is capable of correctly classifying 86.1% of instances, compared to a baseline of 73% which segments the time-series data with fixed-size non-overlapping windows, and an exhaustive-search approach with an accuracy of 92.6%.1

Original languageEnglish (US)
Pages (from-to)96-104
Number of pages9
JournalMicroprocessors and Microsystems
StatePublished - Oct 1 2016


  • Segmentation
  • Support vector machines
  • Time-series
  • Wearable devices

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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