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Studying the effect of modified spectral subtraction algorithm parameters and time window length in speech signals enhancement

دراسة تأثير معاملات خوارزمية الطرح الطيفي المعدَلة و طول النافذة الزمنية في تحسين الإشارات الصوتية

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 Publication date 2015
and research's language is العربية
 Created by Shamra Editor




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Speech denoising is a field of engineering that studies techniques used to recover the original signal from the noisy signal corrupted with different types of noise, such as broadband noise and narrowband noise, and other types present in environment, but the spectral subtraction technique consider the most prominent in this area . In this search we will discuss the parameters impact of the modified spectral subtraction algorithm and the time window length in the enhancement of speech that corrupted with broadband noise. We done the study and determine the ideal parameters values and the ideal window length with different values for the signal -to-noise ratio SNR for noisy speech and we discuss 18 case for each value. We done the simulation using MATLAB software and the results were compared based on improving the value of SNR for each case .


Artificial intelligence review:
Research summary
تتناول هذه الورقة البحثية دراسة تأثير معاملات خوارزمية الطرح الطيفي المعدلة وطول النافذة الزمنية في تحسين الإشارات الصوتية الملوثة بالضجيج عريض الحزمة. تهدف الدراسة إلى تحديد القيم المثلى لهذه المعاملات عند نسب مختلفة للإشارة إلى الضجيج (SNR) وهي 0، 5، 10، 15 ديسيبل. تم إجراء المحاكاة باستخدام برنامج الماتلاب، وتمت مقارنة النتائج سماعياً وبالاعتماد على تحسين قيمة SNR. تتضمن الدراسة تصميم خوارزمية الطرح الطيفي المعدلة، إضافة ضجيج عريض الحزمة إلى إشارة صوتية مسجلة واضحة، واختبار عدة قيم لمعاملات الخوارزمية وطول النافذة الزمنية. أظهرت النتائج أن الخوارزمية تقدم أداءً جيداً في تخفيض الضجيج الخلفي، مع وجود بعض التشويه الطفيف في الكلام المحسن. توصي الدراسة بإعادة البحث باستخدام نوافذ زمنية أخرى وتحسين دقة الكاشف الفعال للصوت.
Critical review
تعتبر هذه الدراسة خطوة مهمة في مجال تحسين الإشارات الصوتية الملوثة بالضجيج، إلا أن هناك بعض النقاط التي يمكن تحسينها. أولاً، كان من الأفضل توسيع نطاق القيم المدروسة لمعاملات الخوارزمية وطول النافذة الزمنية لتشمل نطاقات أوسع. ثانياً، يمكن تحسين دقة الكاشف الفعال للصوت المستخدم في الخوارزمية، مما قد يؤدي إلى تحسين الأداء بشكل أكبر. ثالثاً، لم تتناول الدراسة تأثير أنواع أخرى من الضجيج غير عريض الحزمة، مما يحد من تطبيقات الخوارزمية في بيئات متنوعة. أخيراً، كان من الممكن تضمين معايير أخرى غير SNR لمقارنة الأداء، مثل مقارنة النتائج سماعياً أو رسم المخططات الطيفية.
Questions related to the research
  1. ما هي الخوارزمية المستخدمة في تحسين الإشارات الصوتية الملوثة بالضجيج في هذه الدراسة؟

    الخوارزمية المستخدمة هي خوارزمية الطرح الطيفي المعدلة.

  2. ما هي القيم المدروسة لنسبة الإشارة إلى الضجيج (SNR) في هذه الدراسة؟

    القيم المدروسة لنسبة الإشارة إلى الضجيج هي 0، 5، 10، 15 ديسيبل.

  3. ما هو البرنامج المستخدم في محاكاة الخوارزمية؟

    البرنامج المستخدم في المحاكاة هو برنامج الماتلاب.

  4. ما هي التوصيات التي قدمتها الدراسة لتحسين أداء الخوارزمية؟

    توصي الدراسة بإعادة البحث باستخدام نوافذ زمنية أخرى، تحسين دقة الكاشف الفعال للصوت، وتضمين معايير أخرى غير SNR لمقارنة الأداء.


References used
Kaladharan,N.Speech Enhancement by Spectral Subtraction Method.International Journal of Computer Applications (0975 – 8887) Volume 96– No.13, June 2014
Verteletskaya,E;Simak,B.Noise Reduction Based on Modified Spectral Subtraction Method.IAENG International Journal of Computer Science,38:1, IJCS_38_1_10, February ,2011
Tiwari,N;Pandey,P.Speech Enhancement Using Noise Estimation Based on Dynamic Quantile Tracking for Hearing Impaired Listeners.Proc. 21th National Conference on Communications 2015 (NCC 2015), Mumbai, Feb. 27 - Mar. 1, 2015
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