Linear prediction coefficient conversion device and linear prediction coefficient conversion method

• US 10,714,108 B2
• Filed: 11/14/2018
• Issued: 07/14/2020
• Est. Priority Date: 04/25/2014
• Status: Active Grant

• Current Assignee: NTT Docomo Incorporated (Nippon Telegraph and Telephone Corporation)
• Original Assignee: NTT Docomo Incorporated (Nippon Telegraph and Telephone Corporation)
• Inventors: Naka, Nobuhiko, Ruoppila, Vesa
• Primary Examiner(s): Blankenagel, Bryan S
• Application Number:
  US16/191,104
• Publication Number:
  US 20190080706A1
• Time in Patent Office: 608 Days
• CPC Class Codes: G10L 19/06 Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
• CPC Class Codes: G10L 19/12 the excitation function being a code excitation, e.g. in code excited linear prediction [CELP] vocoders
• CPC Class Codes: G10L 25/12 the extracted parameters being prediction coefficients

Abstract

The purpose of the present invention is to estimate, with a small amount of computation, a linear prediction synthesis filter after conversion of an internal sampling frequency. A linear prediction coefficient conversion device is a device that converts first linear prediction coefficients calculated at a first sampling frequency to second linear prediction coefficients at a second sampling frequency different from the first sampling frequency, which includes a means for calculating, on the real axis of the unit circle, a power spectrum corresponding to the second linear prediction coefficients at the second sampling frequency based on the first linear prediction coefficients or an equivalent parameter, a means for calculating, on the real axis of the unit circle, autocorrelation coefficients from the power spectrum, and a means for converting the autocorrelation coefficients to the second linear prediction coefficients at the second sampling frequency.

Claims

1. A linear prediction coefficient conversion device that converts first linear prediction coefficients calculated at a first sampling frequency F1 to second linear prediction coefficients at a second sampling frequency F2 (where F1<

F2) different from the first sampling frequency, comprising a circuitry configured to;

calculate, on a real axis of a unit circle, a power spectrum corresponding to the second linear prediction coefficients at the second sampling frequency based on the first linear prediction coefficients or an equivalent parameter, wherein the power spectrum is obtained, using the first linear prediction coefficients, at points on the real axis corresponding to N1 number of different frequencies, where frequencies are 0 or more and F1 or less, and (N1−

1)(F2−

F1)/F1 number of power spectrum components corresponding to more than F1 and F2 or less are obtained by using a N1-th power spectrum corresponding to a frequency F1 of the power spectrum calculated using the first linear prediction coefficients;

calculate, on the real axis of the unit circle, autocorrelation coefficients from the power spectrum; and

convert the autocorrelation coefficients to the second linear prediction coefficients at the second sampling frequency.

2. A linear prediction coefficient conversion method performed by a device that converts first linear prediction coefficients calculated at a first sampling frequency F1 to second linear prediction coefficients at a second sampling frequency F2 (where F1<

F2) different from the first sampling frequency, comprising;

a step of calculating, on a real axis of a unit circle, a power spectrum corresponding to the second linear prediction coefficients at the second sampling frequency based on the first linear prediction coefficients or an equivalent parameter, wherein the power spectrum is obtained, using the first linear prediction coefficients, at points on the real axis corresponding to N1 number of different frequencies, where frequencies are 0 or more and F1 or less, and (N1−

1)(F2−

F1)/F1 number of power spectrum components corresponding to more than F1 and F2 or less are obtained by using a N1-th power spectrum corresponding to a frequency F1 of the power spectrum calculated using the first linear prediction coefficients;

a step of calculating, on the real axis of the unit circle, autocorrelation coefficients from the power spectrum; and

a step of converting the autocorrelation coefficients to the second linear prediction coefficients at the second sampling frequency.