Open AccessModified coherence‐based dictionary learning method for speech enhancement
Author(s) -
Mavaddaty Samira,
Ahadi Seyed Mohammad,
Seyedin Sanaz
Publication year - 2015
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2014.0148
Subject(s) - computer science , sparse approximation , coherence (philosophical gambling strategy) , k svd , speech recognition , noise (video) , speech enhancement , artificial intelligence , context (archaeology) , pattern recognition (psychology) , energy (signal processing) , noise reduction , algorithm , mathematics , statistics , paleontology , image (mathematics) , biology
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 multi‐band and geometric spectral subtraction.