Efficient Compressed Sensing Quantization of LSP Parameters Based on the Approximate KLT Domain

For low bit rate speech coding applications, it is very important to quantize the Line Spectrum Pair (LSP) parameters accurately using as few bits as possible without sacrificing the speech quality. In this paper, the sparsity of LSP parameters on the approximated Karhunen-Loeve Transform (KLT) domain is researched, and then an efficient LSP parameters quantization scheme is proposed based on the Compressed Sensing (CS). In the encoder, the LSP parameters extracted from consecutive speech frames are compressed by CS on the approximate KLT domain to produce a low dimensional measurement vector, the measurements are quantized using the split vector quantizer. In the decoder, according to the quantized measurements, the original LSP vector is reconstructed by the orthogonal matching pursuit method, the reconstructed LSP vector is the ultimate quantization value of the original LSP parameters. Experimental results show that the scheme can obtain transparent quality at 5 bit/frame with realistic codebook storage and search complexity.