کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10127512 | 1645056 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Blind reverberation energy estimation using exponential averaging with attack and release time constants for hearing aids
ترجمه فارسی عنوان
برآورد انرژی بازتاب کور با استفاده از میانگین آماری با حمله و زمان ثابت ثابت برای ابزارهای شنوایی
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کلمات کلیدی
سخنرانی حیرت انگیز، برآورد کور، سمعک، میانگین عددی تفریق طیفی،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی مکانیک
چکیده انگلیسی
Dereverberation signal processing is necessary for hearing-aid systems because reverberation degrades speech intelligibility in reverberant environments. In general, hearing-aid systems require low computational cost and real-time signal processing. The spectral subtraction (SS) method is a simple and frequently used technique which is used not only for noise-reduction but for dereverberation as well. To perform dereverberation methods based on SS for hearing aids, the reverberation energy must be estimated or measured. In this paper, the SS-based blind estimation of reverberation energy in a single-channel speech signal is proposed by using exponential averaging with attack and release time constants. The estimation error, which is the difference between the true and estimated reverberation energy, was used for evaluation. The estimation error of the proposed method was compared with the results of the method proposed by Lebart et al., which is a well-known non-blind SS-based dereverberation method. According to the results, the reverberation energy was more accurately estimated when the reverberation time was longer than 0.6â¯s, and the estimation error of the proposed method was approximately half of that of the well-known non-blind method by Lebart.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Acoustics - Volume 142, 15 December 2018, Pages 106-113
Journal: Applied Acoustics - Volume 142, 15 December 2018, Pages 106-113
نویسندگان
Kotoyo Nozaki, Yusuke Ikeda, Yasuhiro Oikawa, Yoh-ichi Fujisaka, Masahiro Sunohara,