Low-Complexity Hardware Design for Fast Solving LSPs With Coordinated Polynomial Solution

Chang, Chung-Hsien; Chen, Bo-Wei; Chen, Shi-Huang; Wang, Jhing-Fa; Chiu, Yu-Hao

doi:10.1109/tvlsi.2014.2305699

Cited by 2 publications

(6 citation statements)

References 30 publications

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“…This section evaluates the performance of the proposed method, including the computation time and the speech quality, by making a comparison between the proposed method and other methods, including the Birge-Vieta method [14], Tschirnhaus Transform [13], original/Modified Ferrari's method [12], Chen and Wu method [11], Soong and Juang [8], and the Full search.…”

Section: Performance Of the Proposed Methodsmentioning

confidence: 99%

“…This section introduces the background of the LSPs frequencies and some of the relevant properties briefly. Given a p-order LPC, the minimum-phase LPC polynomial is expressed as follows, according to Reference [14]:…”

Section: Lpc Polynomialmentioning

confidence: 99%

“…The roots of P(z) and Q(z) determine the value of LSPs frequencies. According to Reference [14], the above two auxiliary polynomials have the significant properties that their roots are on the unit circle and interleave with each other. Further, P(z) has a root z = −1 (w = π), and Q(z) has a root z = 1 (w = 0).…”

Section: Lpc Polynomialmentioning

confidence: 99%

“…Then, we apply our method to a speech compression codec system. Experiments show that, compared with other methods, including those of Soong and Juang [8], Kabal and Ramachandram [10], the Chen and Wu method [11], the modified Ferrari's formula [12], Tschirnhaus transform [13], the Birge-Vieta method [14] and the previous Braistow-base LPC analysis methods [15,16], the proposed method can estimate the LSPs frequencies accurately with the lowest computational complexity, together with high speech quality.…”

Section: Introductionmentioning

confidence: 99%

“…While it can accelerate the computation processing, it still needs to compute trigonometric functions. Chung-Hsien Chang [14] proposed a method to solve the LPC polynomials, which is suitable for hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

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Fast Computation of LSP Frequencies Using the Bairstow Method

et al. 2020

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Linear prediction is the kernel technology in speech processing. It has been widely applied in speech recognition, synthesis, and coding, and can efficiently and correctly represent the speech frequency spectrum with only a few parameters. Line Spectrum Pairs (LSPs) frequencies, as an alternative representation of Linear Predictive Coding (LPC), have the advantages of good quantization accuracy and low spectral sensitivity. However, computing the LSPs frequencies takes a long time. To address this issue, a fast computation algorithm, based on the Bairstow method for computing LSPs frequencies from linear prediction coefficients, is proposed in this paper. The algorithm process first transforms the symmetric and antisymmetric polynomial to general polynomial, then extracts the polynomial roots. Associated with the short-term stationary property of speech signal, an adaptive initial method is applied to reduce the average iteration numbers by 26%, as compared to the statics in the initial method, with a Perceptual Evaluation of Speech Quality (PESQ) score reaching 3.46. Experimental results show that the proposed method can extract the polynomial roots efficiently and accurately with significantly reduced computation complexity. Compared to previous works, the proposed method is 17 times faster than Tschirnhus Transform, and has a 22% PESQ improvement on the Birge-Vieta method with an almost comparable computation time.

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