ISSN 0914-4935

Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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High-efficiency Vector Quantization Codebook Search Algorithms for Extended Adaptive Multi-rate-wideband Audio Coder

Cheng-Yu Yeh and Hsiang-Yueh Lai

(Received May 22, 2018; Accepted September 5, 2018)

Keywords: audio codec, vector quantization (VQ), immittance spectral frequency (ISF), triangular inequality elimination (TIE), equal-average nearest neighbor search (ENNS)

The extended adaptive multi-rate-wideband (AMR-WB+) is a standard audio codec stipulated by 3rd Generation Partnership Project (3GPP). It has been experimentally validated by 3GPP that an AMR-WB+ audio codec well outperforms others when encoding speech or audio signals at bitrates below 24 kbps, but at the cost of high computational load, particularly when performing the vector quantization (VQ) of immittance spectral frequency (ISF) coefficients. For this sake, by no means can an AMR-WB+ audio codec implemented on mobile devices meet the energy efficiency requirement. As a solution to this problem, in this paper, we present two high-efficiency triangular inequality elimination (TIE)-based algorithms for VQ codebook search. The first is referred to as the iterative TIE (ITIE) search algorithm, and the other is substantially a combined use of ITIE and the equal-average nearest neighbor search (ENNS) algorithm, both of which are subsequently applied to quantize the vectors of ISF coefficients. Most of the codewords are ruled out herein as the candidates using one-level and two-level rejection criteria, leading to a significantly reduced number of codeword searches but still maintains the same coding quality. With a full search algorithm as a benchmark, this work gives a reduction in the total number of operations above 75.21%, a figure far beyond 60.23% using the TIE algorithm with a dynamic and an intersection mechanism (DI-TIE), 60.85% using ENNS, and 64.88% using the equal-average equal-variance equal-norm nearest neighbor search (EEENNS) algorithm. Moreover, this work can be implemented on handheld devices equipped with multiple sensing components, e.g., smartphones, to provide various multimedia and audio applications, and the energy saving requirement can be achieved as well.

Corresponding author: Hsiang-Yueh Lai




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