An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec

Abstract

Adaptive multi-rate wideband (AMR-WB) speech codecs have been widely used for high speech quality in modern mobile communication systems, e.g., handheld mobile devices. Nevertheless, a major handicap is that a remarkable computational load is required in the vector quantization (VQ) of immittance spectral frequency (ISF) coefficients of an AMR-WB coding. In view of this, a two-stage search algorithm is presented in this paper as an efficient way to reduce the computational complexity of ISF quantization in AMR-WB coding. At stage 1, an input vector is assigned to a search subspace in an efficient manner using the binary search space-structured VQ (BSS-VQ) algorithm, and a codebook search is performed over the subspace at stage 2 using the iterative triangular inequality elimination (ITIE) approach. Through the use of the codeword rejection mechanisms equipped in both stages, the computational load can be remarkably reduced. As compared with the original version of the BSS-VQ algorithm, the upgraded version provides a computational load reduction of up to 51%. Furthermore, this work is expected to satisfy the energy saving requirement when implemented on an AMR-WB codec of mobile devices.

An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

“…Compared to the TIE and ITIE methods, the proposed method provided CS of up to 76% and 63% with almost the same quantization accuracy. The computational load and the quantization accuracy are compared for the proposed algorithm with TIE [18], ITIE [21], and BSS-ITIE [28] approaches. With the performance of the full search algorithm as the benchmark, Table 4 gives the comparison of ER, ASN, and CS for the proposed algorithm with TIE [18], ITIE [21], and BSS-ITIE [28] approaches.…”

“…The computational load and the quantization accuracy are compared for the proposed algorithm with TIE [18], ITIE [21], and BSS-ITIE [28] approaches. With the performance of the full search algorithm as the benchmark, Table 4 gives the comparison of ER, ASN, and CS for the proposed algorithm with TIE [18], ITIE [21], and BSS-ITIE [28] approaches. The experimental results show that the proposed algorithm provided CS of up to 92% when TQA = 0.90 and when TQA = 0.99, it still reduced the computational load by 85%.…”

“…Even though the input vectors are uncorrelated, it can still reduce the computational load significantly while maintaining the quantization accuracy. Compared with previous works on the full search algorithm [6], TIE [18], ITIE [21], and the BSS-ITIE [28], the experimental results show that the proposed algorithm can quantize the input vector with the lowest computational load.…”

“…However, the full search algorithm demands a large computation load. To reduce the encoding search complexity, many approaches have been proposed [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. These approaches can be classified into four main types, according to their base techniques.…”

“…These approaches can be classified into four main types, according to their base techniques. The first type is the tree-structured VQ (TSVQ) technique [13][14][15][16][17], the second type is the triangular inequality elimination (TIE)-based approach [18][19][20][21], the third type is the equal-average equal-variance equal-norm nearest neighbor search (EEENNS) method [22][23][24] based on the statistical characteristic values of the input signal, and the last type is the binary search space-structured VQ (BSS-VQ) [25][26][27][28] method.…”