آستانه‌گذاري وفقي ضرائب موجك برای پاکسازی سیگنال گفتار نویزی
محورهای موضوعی : مهندسی برق و کامپیوترفاطمه شیخ علیشاهی 1 , حمیدرضا ابوطالبی 2 , محمدرضا تابان 3
1 - دانشگاه یزد
2 - دانشگاه یزد
3 - دانشگاه يزد
کلید واژه: بهسازي گفتارتبديل موجكآستانه‌گذاري وفقی,
چکیده مقاله :
اين مقاله به مبحث بهسازي گفتار در حوزه موجك مي‌پردازد. در روش پیشنهادی، بعد از تجزيه سيگنال نويزي به باندهاي موجك تابع آستانه‌گذاري وفقي روي ضرايب موجك اعمال مي‌شود. در زيرباندهایی كه دارای انرژی گفتار با محتوای بسیار زیاد هستند، از حد آستانه كوچك‌تر و تابع آستانه‌گذاري سخت استفاده می‌شود و برعکس، در زيرباندهاي با محتوای ناچیز از انرژی گفتار، حد آستانه بزرگ‌تر و تابع آستانه‌گذاري نرم مورد استفاده واقع می‌شود. در نواحی با وضعیت بینابین دو حالت فوق، تابع آستانه‌گذاري به‌صورت وفقی و مابين دو وضعیت حدی آستانه‌گذاري سخت و آستانه‌گذاری نرم تعیین می‌شود. پارامتري كه تابع آستانه‌گذاري و حد آستانه را در هر زيرباند موجك تعيين مي‌كند با نسبت توان گفتار و نويز در هر زیرباند رابطه دارد. آزمایش‌های انجام‌شده در مقایسه با روش‌های قبلی نشان می‌دهد كه با اعمال اين تكنيك، نويز به‌نحو مطلوبي حذف شده و میزان اعوجاج در گفتار خروجي کاهش می‌یابد. علاوه بر اين، نتايج شبيه‌سازي حکایت از آن دارد كه افزايش رشد درخت موجك در بهبود خروجي سيستم بهسازي تأثير داشته و نوع موجك مناسب، وابسته به نوع نویز موجود در محیط می‌باشد.
This paper addresses the problem of speech enhancement in wavelet domain. After decomposition of noisy signal into wavelet sub-bands, an adaptive thresholding process is applied on wavelet coefficients. In the proposed technique, small threshold value and hard thrsholding function are used in sub-bands with high speech energy; vice versa, in sub-bands with low speech energy, large threshold value and soft thresholding function are employed. For other sub-bands (between above two extreme cases for speech energy), we use an adaptive thresholding function that is actually between soft- and hard-thresholding functions. The threshold value and thresholding function are determined by a parameter related to the ratio of speech and noise powers in each sub-band. Our extensive experiments show the superiority of proposed method in removing the background noise and reduction of speech distortion. It was also shown that both wavelet tree structure and wavelet type affect on the performance of speech de-noising system.
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