LSF coefficient vector quantizer for wideband speech coding
First Claim
1. A line spectral frequency coefficient vector quantizer comprising:
- a prediction structure quantizer that comprises a first vector quantizer which non-structurally quantizes a line spectral frequency coefficient vector to calculate a candidate vector to be quantized, a predictor which calculates a predicted line spectral frequency vector of the line spectral frequency coefficient vector, and a first lattice quantizer which lattice-quantizes the candidate vector with reference to the predicted line spectral frequency vector to calculate a final prediction quantization vector of the line spectral frequency coefficient vector;
a non-prediction structure quantizer that comprises a second vector quantizer which non-structurally quantizes the line spectral frequency coefficient vector to calculate a candidate vector to be quantized and a second lattice quantizer which lattice-quantizes the candidate vector to calculate a final non-prediction quantization vector of the line spectral frequency coefficient vector; and
a switch that determines one having a small difference from the line spectral frequency coefficient vector, from the final prediction quantization vector and the final non-prediction quantization vector, as a final quantization vector of the line spectral frequency coefficient vector.
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Abstract
A line spectral frequency (LSF) coefficient vector quantizer greatly affects wideband speech coding efficiency and performance. An LSF coefficient quantizer of an existing speech codec can be modified into a new structure in which a non-structural vector quantizer and a lattice quantizer are connected in series. Thus, memory capacity and search time required for the LSF coefficient quantizer can be reduced. In addition, a prediction structure and a non-prediction structure can be connected in parallel to stably perform quantization and reduce a quantization transfer error. As a result, an efficient LSF quantizer capable of reducing allocated bits and improving SD can be provided. Moreover, non-structural vector quantization can be performed prior to pyramid vector quantization to convert an input value into a Laplacian model suitable for a pyramid vector quantizer. Also, a high-performance quantizer can be provided by determining a joint optimisation vector between two serial quantizers using a small amount of computation of the pyramid vector quantizer. Furthermore, outliers unsuitable for the prediction structure can be correctly quantized by adopting the prediction structure and the non-prediction structure.
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Citations
6 Claims
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1. A line spectral frequency coefficient vector quantizer comprising:
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a prediction structure quantizer that comprises a first vector quantizer which non-structurally quantizes a line spectral frequency coefficient vector to calculate a candidate vector to be quantized, a predictor which calculates a predicted line spectral frequency vector of the line spectral frequency coefficient vector, and a first lattice quantizer which lattice-quantizes the candidate vector with reference to the predicted line spectral frequency vector to calculate a final prediction quantization vector of the line spectral frequency coefficient vector;
a non-prediction structure quantizer that comprises a second vector quantizer which non-structurally quantizes the line spectral frequency coefficient vector to calculate a candidate vector to be quantized and a second lattice quantizer which lattice-quantizes the candidate vector to calculate a final non-prediction quantization vector of the line spectral frequency coefficient vector; and
a switch that determines one having a small difference from the line spectral frequency coefficient vector, from the final prediction quantization vector and the final non-prediction quantization vector, as a final quantization vector of the line spectral frequency coefficient vector. - View Dependent Claims (2, 3, 4, 5, 6)
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Specification