1403/01/09
سمیرا مودتی

سمیرا مودتی

مرتبه علمی: استادیار
ارکید:
تحصیلات: دکترای تخصصی
اسکاپوس:
دانشکده: دانشکده مهندسی و فناوری
نشانی:
تلفن: 011-35305126

مشخصات پژوهش

عنوان
Modified coherence-based dictionary learning method for speech enhancement
نوع پژوهش
JournalPaper
کلیدواژه‌ها
Coherence-based dictionary learning, Speech enhancement, Moedel learning
سال
2015
مجله IET Signal Processing
شناسه DOI
پژوهشگران Samira Mavaddati ، Mohammad Ahadi ، Sanaz Seyedin

چکیده

This paper presents a new method for speech enhancement based on a dictionary learning method. The proposed approach is based on using coherence measure in dictionary learning. Data required for better fitting to atoms in sparse representation of noise is provided by a noise estimation algorithm that causes noise dictionary to be trained with the same data size as speech signal. To decrease coherence between dictionaries after the training step, a new method is applied to yield incoherent dictionaries. In sparse representation of speech data, the highest energy atoms of noise dictionary are replaced with the lowest energy atoms, under certain conditions. A similar replacement can happen in sparse representation of noise data. Furthermore, in this paper, only one noise dictionary, chosen by a classification method, is used in speech enhancement step, resulting in a faster algorithm. Objective and subjective measures are used for evaluating the simulation results. According to experimental results, the proposed algorithm has been found superior in performance and computation overhead in comparison with the earlier methods in this context. Moreover, this method achieves significantly better results compared with baseline methods such as multiband and geometric spectral subtraction.