کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6864842 1439552 2018 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Non-stationary sources separation based on maximum likelihood criterion using source temporal-spatial model
ترجمه فارسی عنوان
جداسازی منابع غیر سازمانی بر اساس معیار حداکثر احتمال با استفاده از مدل زمانی-فضایی منبع
کلمات کلیدی
جداسازی منبع کور، حداکثر احتمال، روش به حداکثر رساندن امید، پردازش سیگنال غیر ثابت، مدل خودمراقبتی، مدل مخفی مارکف، 00-01، 99-00،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
In this paper, we propose a new non-stationary sources separation algorithm, which is referred to as autoregressive hidden Markov Gaussian process (AR-HMGP), in which the sources are non-stationary and temporally correlated. For the proposed algorithm, a generative model is employed to track the non-stationarity of the source where the temporal dependencies of sources are represented by autoregressive model (AR) and the distribution of the associated innovation process is described using non-stationary Gaussian process with hidden Markov model (HMM). We further explore the maximum likelihood (ML) method to estimate the parameters of the source model by using the expectation maximum (EM) algorithm. Our important findings reveal that (a) AR-HMGP algorithm outperforms the other three BSS algorithms for non-stationary sources separation, the instantaneous mixture system is also well corroborated with the effectiveness of our algorithm; (b) both independent and dependent non-stationary sources have been successfully separated; (c) the proposed algorithm is robust with respect to noise, while the other three algorithms are not.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 275, 31 January 2018, Pages 341-349
نویسندگان
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