Method and device for robust predictive vector quantization of linear prediction parameters in variable bit rate speech coding

• US 20050261897A1
• Filed: 01/19/2005
• Published: 11/24/2005
• Est. Priority Date: 12/24/2002
• Status: Active Grant

• Current Assignee: Nokia Technologies Oy (Nokia Corporation)
• Original Assignee: Nokia Corporation
• Inventors: Jelinek, Milan
• Granted Patent: US 7,149,683 B2
• Time in Patent Office: Days
• US Class Current: 704/219
• CPC Class Codes: G10L 19/038 Vector quantisation, e.g. TwinVQ audio
• CPC Class Codes: G10L 19/20 using sound class specific coding, hybrid encoders or object based coding

Abstract

The present invention relates to a method and device for quantizing linear prediction parameters in variable bit-rate sound signal coding, in which an input linear prediction parameter vector is received, a sound signal frame corresponding to the input linear prediction parameter vector is classified, a prediction vector is computed, the computed prediction vector is removed from the input linear prediction parameter vector to produce a prediction error vector, and the prediction error vector is quantized. Computation of the prediction vector comprises selecting one of a plurality of prediction schemes in relation to the classification of the sound signal frame, and processing the prediction error vector through the selected prediction scheme. The present invention further relates to a method and device for dequantizing linear prediction parameters in variable bit-rate sound signal decoding, in which at least one quantization index and information about classification of a sound signal frame corresponding to the quantization index are received, a prediction error vector is recovered by applying the index to at least one quantization table, a prediction vector is reconstructed, and a linear prediction parameter vector is produced in response to the recovered prediction error vector and the reconstructed prediction vector. Reconstruction of the prediction vector comprises processing the recovered prediction error vector through one of a plurality of prediction schemes depending on the frame classification information.

Claims

1. Apparatus comprising a switched predictive vector quantizer having an input for receiving an input Linear Prediction (LP) parameter vector z and a first processor for removing a vector of mean LP parameters μ

from the input LP parameter vector z to produce a mean-removed LP parameter vector x, a second processor for determining a prediction vector p and a third processor for removing the prediction vector p from the mean-removed LP parameter vector x to produce a prediction error vector e, further comprising a fourth processor responsive to frame classification information such that if a frame corresponding to the input LP parameter vector z is stationary voiced then autoregressive (AR) prediction is used and the error vector e is scaled by a certain factor to obtain a scaled prediction error vector e′

, whereas if the frame is not stationary voiced moving average (MA) prediction is used and the scaling factor is equal to one;

further comprising a fifth processor coupled to receive the scaled prediction error vector e′ and

operable to vector quantize the scaled prediction error vector e′

to produce a quantized scaled prediction error vector ê

′ and

a sixth processor coupled to receive the quantized scaled prediction error vector ê

′

for applying a scaling inverse to that applied by said fourth processor to the quantized scaled prediction error vector ê

′

to produce the quantized prediction error vector ê

;

where said second processor determines the prediction vector p in one of an MA predictor and an AR predictor depending on the frame classification information such that if the frame is stationary voiced then the prediction vector p is equal to the output of the AR predictor else the prediction vector p is equal to the output of the MA predictor, where said MA predictor operates on quantized prediction error vectors from previous frames and said AR predictor operates on quantized input LP parameter vectors from previous frames; and

where the quantized input LP parameter vector (mean-removed) is constructed by adding the quantized prediction error vector ê

to the prediction vector p;

{circumflex over (x)}=ê

+p.