تحسين إشارة الصوت باستخدام مرشح كالمان الممتد باعتماد الشبكات العصبية
Abstract
إن مسألة تخفيض الضجيج في إشارات الكلام تعتبر ذات أهمية كبيرة في التطبيقات المختلفة بدءاً من نظم التعرف على الصوت، إلى تحسين الاتصالات عن بعد في مجال الطيران المدني والعسكري وإجراء المؤتمرات عن بعد، والاتصالات الخليوية.إن الهدف هو تحسين نوعية إشارة الكلام المستقبلة أو زيادة انجابيتها. وعادةً ما تستخدم الطرق الطيفية لتحقيق هذا الغرض في مثل هذه التطبيقات. ولكنها غالباً ما تترافق بتشوهات في المجال السمعي.
سوف نعرض في هذا المقال طريقة للترشيح في مجال الزمن غير خطية تعرف باسم مرشح كالمان ثنائي الامتداد ونبين أنها تمتلك مزايا هامةفي مجال ترشيح إشارة الصوت من الضجيج المتغير والضجيج الملون, إن المنهج المتبع يستخدم نموذج متنبئ مترافق مع مرشح كالمان الممتد وذلك باعتماد الشبكات العصبية الصنعية في المجال العقدي من خلال عرض خوارزمية المرشح مع بعض النتائج العملية.
The removal of noise from speech signals has important applications ranging from speech recognition systems, to enhancement of tele-communications in aviation, military, teleconferencing, and cellular environments. the goal is either to improve the perceived quality of the speech, or to increase its intelligibility. Spectral techniques are commonly used in these applications, but frequently result in audible distortion of the signal. A nonlinear time-domain method called dual extended Kalman filtering(DEKF) is presented, that demonstrates significant advantages for removing nonstationary and Colored noise from speech. the approach uses a predictive model in conjunction with an extended Kalman filtring based on neural networks in complex domain. This paper describes the algorithm and some experimental results.
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