Simultaneous speech coding and de-noising in a dictionary based quantized CS framework

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• 作者：Vinitha Ramdas ; Sai Subrahmanyam R. K. Gorthi…
• 刊名：International Journal of Speech Technology
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：19
• 期：3
• 页码：509-523
• 全文大小：1,361 KB
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Social Sciences
  Artificial Intelligence and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1572-8110
• 卷排序：19

文摘

Speech compression or speech coding is inevitable for effective communication of speech signals in resource limited scenarios and researcher’s have been working on achieving lower and lower transmission bit rates (BR) without much compromise on the quality of speech. Medium BR hybrid speech coding schemes have gained much interest in the recent years with most of them based on CELP, the basic medium bit-rate coding scheme. In this work, we provide an insight to the capabilities of compressive sensing (CS) in speech processing and propose a novel idea in the quantized framework. Three major aspects demonstrated in this paper are (1) Inherent de-noising of noisy speech by the CS based coder along with compression (2) Quantization of CS measurements to achieve medium transmission bit-rates and (3) Enhancement of quality and compression performance of the coder with better sparse representations of speech using dictionaries. The results indicate that the proposed scheme offers better compression in comparison with basic Gaussian codebook CELP. The CS scheme has the added advantage of inherent noise suppression and provides more robustness to background noise in comparison with parameter extraction based medium bit-rate speech coding systems.KeywordsSpeech codingCompressive sensingAnalysis-by-synthesis quantizationHybrid dictionary