کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4956860 | 1364713 | 2016 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Probabilistic segmentation of time-series audio signals using Support Vector Machines
ترجمه فارسی عنوان
تقسیم بندی احتمالی سیگنال های صوتی سری زمانی با استفاده از دستگاه های بردار پشتیبانی
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کلمات کلیدی
تقسیم بندی، سری زمانی، ماشین آلات بردار پشتیبانی، دستگاه های پوشیدنی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
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
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Microprocessors and Microsystems - Volume 46, Part A, October 2016, Pages 96-104
Journal: Microprocessors and Microsystems - Volume 46, Part A, October 2016, Pages 96-104
نویسندگان
Haik Kalantarian, Bobak Mortazavi, Mohammad Pourhomayoun, Nabil Alshurafa, Majid Sarrafzadeh,