A comparative study between compressive sensing and conventional speech conding methods

Speech coding is an essential procedure in public switched telephone system (PSTN), digital cellular communications, videoconferencing systems, and emerging voice over Internet applications. Compressed sensing is an original signal processing tool for efficiently acquiring and reconstructing a signal, by exploiting its compressibility. In this paper, compressive sensing is employed for speech coding. Particularly in this work and in order to demonstrate its efficiency in speech coding, we propose a comparative study between this method and optimal existing methods, namely, Code-excited linear prediction and LIoyd-Max quantization algorithm. Experimental results indicate that CS method achieve a significant improvements in performances with respect to the aforementioned methods.

• compressed sensing
• speech coding
• CELP
• Lloyd-Max quantization
• signal reconstruction

• Sparse and Compressive Sensing Techniques
• Speech and Audio Processing
• Advanced Data Compression Techniques
• compressed sensing
• speech coding
• CELP
• Lloyd-Max quantization
• signal reconstruction
• Computational Mechanics
• Signal Processing
• Computer Vision and Pattern Recognition
• Engineering
• Computer Science