# US 10,504,532 B2 · Method and device for quantizing linear predictive coefficient, and method and device for dequantizing same > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,504,532 B2 - Document ID: US10504532B2 - Status: Granted - Kind: B2 - Application number: 15/309,334 - Filed: 2015-05-07 - Published: 2017-06-01 - Granted: 2019-12-10 - Earliest claimed: 2014-05-07 - Assignees: Samsung Electronics Co., Ltd.; Industry-University Cooperation Foundation Hanyang University Erica Campus - Applicants: Samsung Electronics Co., Ltd.; Industry-University Cooperation Foundation Hanyang University Erica Campus - Inventors: Ho-sang Sung; Sang-won Kang; Jong-hyun Kim; Eun-mi Oh - Web page: https://patentgrep.com/patent/US10504532B2 ## CPC - G10L 19/038: Vector quantisation, e.g. TwinVQ audio - G10L 19/07: Line spectrum pair [LSP] vocoders - G10L 19/022: Blocking, i.e. grouping of samples in time; Choice of analysis windows; Overlap factoring - G10L 19/04: using predictive techniques - G10L 19/06: Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients - G10L 2019/0004: {Design or structure of the codebook} - G10L 2019/0016: {Codebook for LPC parameters} - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract A quantization device includes: a trellis-structured vector quantizer which quantizes a first error vector between an N-dimensional (here, “N” is two or more) subvector and a first predictive vector; and an inter-frame predictor which generates a first predictive vector from the quantized N-dimensional subvector, wherein the inter-frame predictor uses a predictive coefficient comprising an N×N matrix and performs an inter-frame prediction using the quantized N-dimensional subvector of a previous stage. ## Claims ### Claim 1 A quantization apparatus comprising: a trellis-structured vector quantizer configured to quantize a first error vector of a current frame which corresponds to a difference between a first prediction vector of the current frame and an N-dimension sub-vector of the current frame, where N is a natural number greater than or equal to 2; an intra-frame predictor configured to generate the first prediction vector of the current frame from a quantized N-dimension sub-vector of a the current frame; and a vector quantizer configured to quantize a quantization error vector of the current frame which corresponds to a difference between the quantized N-dimension sub-vector of the current frame and the N-dimension sub-vector of the current frame, wherein the intra-frame predictor is configured to use a prediction coefficient with an N×N matrix and to perform intra-frame prediction by using the quantized N-dimension sub-vector of the current frame. ### Claim 2 (depends on 1) The apparatus of claim 1, further comprising an inter-frame predictor configured to generate a second prediction vector of the current frame from a quantized N-dimension sub-vector of a previous frame, wherein when the trellis-structured vector quantizer is configured to quantize a second error vector of the current frame which corresponds to a difference between a prediction error vector of the current frame and a second vector of the current frame, the prediction error vector being obtained from the N-dimension sub-vector and the second prediction vector of the current frame. ### Claim 3 (depends on 2) The apparatus of claim 2, wherein the vector quantizer is configured to quantize a quantization error for the prediction error vector of the current frame. ### Claim 4 (depends on 1, 2) The apparatus of claim 1 or 2, wherein the trellis-structured vector quantizer is configured to search for an optimal index based on a weighting function. ### Claim 5 (depends on 3) The apparatus of claim 3, wherein the vector quantizer is configured to search for an optimal index based on a weighting function. ### Claim 6 A quantization apparatus comprising: a first quantization module for performing quantization without an inter-frame prediction; and a second quantization module for performing quantization with an inter-frame prediction, wherein the first quantization module comprises: a first trellis-structured vector quantizer configured to quantize a first error vector of a current frame which corresponds to a difference between a first prediction vector of the current frame and an N-dimension sub-vector of the current frame, where N is a natural number greater than or equal to 2; a first intra-frame predictor configured to generate the first prediction vector of the current frame from a quantized N-dimension sub-vector of the current frame; and a first vector quantizer configured to quantize a quantization error vector of the current frame which corresponds to a difference between the quantized N-dimension sub-vector of the current frame and the N-dimension sub-vector of the current frame, wherein the first intra-frame predictor is configured to use a prediction coefficient with an N×N matrix and to perform intra-frame prediction by using the quantized N-dimension sub-vector of the current frame. ### Claim 7 (depends on 6) The apparatus of claim 6 further comprising a selector configured to select one of the first quantization module and the second quantization module in an open loop manner. ### Claim 8 (depends on 6) The apparatus of claim 6, wherein the second quantization module further comprises a second vector quantizer configured to quantize a quantization error for a prediction error vector of the current frame. ### Claim 9 (depends on 6) The apparatus of claim 6, wherein the first trellis-structured vector quantizer is configured to search for an optimal index based on a weighting function. ### Claim 10 The apparatus of 8, wherein the first vector quantizer or the second vector quantizer is configured to search for an optimal index based on a weighting function. ### Claim 11 The apparatus of 8, wherein the first vector quantizer or the second vector quantizer is configured to share a codebook. ### Claim 12 A quantization apparatus comprising: an intra-frame predictor configured to generate a prediction vector of a current stage from a quantized N-dimension linear vector of a previous stage and a prediction matrix of the current stage; and a vector quantizer configured to generate a first quantized error vector by quantizing a first error vector which corresponds to a difference between the prediction vector of the current stage and an N-dimension linear vector of the current stage, wherein a linear vector of the previous stage is generated based on an error vector of the previous stage and a prediction vector of the previous stage. ### Claim 13 (depends on 12) The apparatus of claim 12 further comprising an error vector quantizer configured to generate a quantized quantization error vector by quantizing a quantization error vector which corresponds to a difference between a quantized N-dimension linear vector of the current stage and an input N-dimension linear vector. ### Claim 14 (depends on 12) The apparatus of claim 12, wherein the intra-frame predictor is configured to generate the prediction vector of the current stage from a quantized prediction error vector of the previous stage and the prediction matrix of the current stage, when the vector quantizer is configured to generate a second quantized error vector by quantizing a second error vector which corresponds to a difference between a prediction error vector and the prediction vector of the current stage, the prediction error vector being obtained from the prediction vector of the current stage and the N-dimension linear vector of the current stage. ### Claim 15 (depends on 14) The apparatus of claim 14 further comprising an error vector quantizer configured to quantize a quantization error for the prediction error vector. ### Claim 16 (depends on 14, 15) The apparatus of claim 14 or 15, wherein the vector quantizer is configured to search for an optimal index based on a weighting function. ### Claim 17 (depends on 15) The apparatus of claim 15, wherein the error vector quantizer is configured to search for an optimal index based on a weighting function. ### Claim 18 A quantization apparatus comprising: a first quantization module for performing quantization without an inter-frame prediction; and a second quantization module for performing quantization with an inter-frame prediction, wherein the first quantization module comprises: a first intra-frame predictor configured to generate a first prediction vector of a current stage from a quantized N-dimension linear vector of a previous stage and a first prediction matrix of the current stage; and a first vector quantizer configured to generate a first quantized error vector by quantizing a first error vector which corresponds to a difference between the first prediction vector of the current stage and an N-dimension linear vector of the current stage, wherein a linear vector of the previous stage is generated based on an error vector of the previous stage and a prediction vector of the previous stage. ### Claim 19 (depends on 18) The apparatus of claim 18, wherein the second quantization module comprises: a second intra-frame predictor configured to generate a second prediction vector of the current stage from a quantized prediction error vector of the previous stage and a second prediction matrix of the current stage; and a second vector quantizer configured to generate a second quantized error vector by quantizing a second error vector which corresponds to a difference between the second prediction vector of the current stage and a prediction error vector, the prediction error vector being obtained from the second prediction vector of the current stage and the N-dimension linear vector of the current stage. ### Claim 20 (depends on 18) The apparatus of claim 18 further comprising a selector configured to select one of the first quantization module and the second quantization module in an open loop manner. ### Claim 21 (depends on 18) The apparatus of claim 18, wherein the first quantization module further comprises a first error vector quantizer configured to generate a quantized quantization error vector by quantizing a quantization error vector which corresponds to a difference between a quantized N-dimension linear vector of the current stage and an input N-dimension linear vector. ### Claim 22 (depends on 19) The apparatus of claim 19, wherein the second quantization module further comprises a second error vector quantizer configured to quantize a quantization error for the prediction error vector. ### Claim 23 (depends on 19) The apparatus of claim 19, wherein the first vector quantizer or the second vector quantizer is configured to search for an optimal index based on a weighting function. ### Claim 24 (depends on 19) The apparatus of claim 19, wherein the first vector quantizer or the second vector quantizer is configured to share a codebook.