DESCRIPTION
LPC VECTOR QUANTIZATION APPARATUS
Technical Field
The present invention relates to an LPC vector
quantization apparatus applicable to a speech
coder/decoder used to enhance transmission efficiency
of a speech signal in the fields of a packet communication
system represented by Internet communication and mobile
communication system, etc.
Background Art
When a speech signal is transmitted in a packet
communication system represented by Internet
communication or mobile communication system, a
compression/coding technology is often used to enhance
transmission efficiency of the speech signal. Many
speech coding systems have been developed so far, and
many lowbit rate speech coding systems developed in recent
years separate a speech signal into a spectral envelope
information and a sound source information andi
compress/code the separated information pieces. For
example, a CELP system described in Document 1
(M.R.Schroeder, B.S.Atal, "Code Excited Linear.
Prediction: High Quality Speech at Low Bit. Rate" IEEE
proc, ICASSP'85 pp.937-940) is one of its examples.
Here, an overview of a CELP-based speech coder will
be explainedusing FIG.l. Suppose an input speech signal
is input to a speech coder successively every processing
frame delimited by a time interval of approximately 20
ms.
The input speech signal input to the speech coder
for every processing frame is supplied to an LPC analysis
section 11 first. The LPC analysis section 11 carries
out an LPC (Linear Predictive Coding) analysis on the
input speech signal, obtains an LPC vector having LPC
coefficients as vector components, vector-quantizes the
LPC vector obtained to obtain an LPC code, and decodes
this LPC code to obtain a decoded LPC vector having decoded
LPC coefficients as vector components.
An excitation vector generation section 14 reads
an adaptive codevector and fixed codevector from an
adaptive codebook 12 and a fixed codebook 13 respectively
and sends those codevectors to an LPC synthesis filter
15. The LPC synthesis filter 15 performs synthesis
filtering on the adaptive codevector and the fixed
codevector supplied from the excitation vector generation
section 14 using an all-pole model synthesis filter having
the decoded LPC coefficients given from the LPC analysis
section 11 as filter coefficients and obtains a
synthesized adaptive codevector and a synthesized fixed
codevector, respectively.
A comparison section 16 analyzes a relationship
between the synthesized adaptive codevector, the
synthesized fixed codevector output from the LPC
synthesis filter 15 and the input speech signal, and
calculates an adaptive codebook optimum gain to be
multiplied on the synthesized adaptive codevector and
a fixed codebook optimum gain to be multiplied on the
synthesized fixed codevector, respectively.
Furthermore, the comparison section 16 adds up the vector
obtained by multiplying the synthesized adaptive
codevector by the adaptive codebook optimum gain and the
vector obtained by multiplying the synthesized fixed
codevector by the fixed codebook optimum gain to obtain
a synthesized speech vector and calculates a distortion
between the synthesized speech vector obtained and input
speech signal.
The comparison section 16 further calculates
distortions between many synthesized speech vectors
obtained by operating the excitation vector generation
section 14 and LPC synthesis filter 15 on all possible
combinations of adaptive codevectors stored in the
adaptive codebook 12 and fixed codevectors stored in the
fixed codebook 13, and the input speech signal, determines
an index of an adaptive codevector and an index of a fixed
codevector that minimize the distortions from among those
codevectors and sends the indices of the codevectors
output from the respective codebooks, codevectors
corresponding to the indices and an adaptive codebook
optimum gain and fixed codebook optimum gain
corresponding to the indices to a parameter coding section
The parameter coding section 17 codes the adaptive
codebook optimum gain and fixed codebook optimum gain
to obtain gain codes, and outputs the gain codes obtained,
the LPC code given from the LPC analysis section 11 and
the indices of the respective codebooks together for each
processing frame.
The parameter coding section 17 further adds up two
vectors; a vector obtained by multiplying the adaptive
codevector corresponding to the index of the adaptive
codebook by an adaptive codebook gain corresponding to
the gain code and a vector obtained by multiplying the
fixed codevector corresponding to the index of the fixed
codebook by a fixed codebook gain corresponding to the
gain code, thereby obtains an excitation vector and
updates the old adaptive codevector in the adaptive
codebook 12 with the excitation vector obtained.
For synthesis filtering by the LPC synthesis filter
15, it is a general practice that linear predictive
coefficients, high-pass filter and perceptual weighting
filter using a long-term predictive coefficient obtained
by carrying out a long-term predictive analysis on the
input speech are used together. It is also a general
practice that a search for optimum indices of the adaptive
codebook and fixed codebook, calculation of optimum gains
and coding processing of optimum gains are carried out
in units of a subframe obtained by subdividing a frame.
Next, an overview of processing of "vector
quantization of LPC vector" carried out by the LPC analysis
section 11 will be explained in more detail using FIG.2.
Suppose that an LPC codebook 22 stores a plural entries
of typical LPC vectors acquired beforehand by applying
the LBG algorithm to many LPC vectors obtained by actually
carrying out an LPC analysis on input speech signals of
many processing frames . Withregardto theLBG algorithm,
the details of its technology are disclosed in Document
2 (Y. Linde, A. Buzo, R. M. Gray, "An Algorithm for Vector
Quantizer Design," IEEE trans. Comm., Vol. COM-28, No.
1, pp84-95, Jan., 1980).
A quantization target vector input to the vector
quantizer in FIG.2 (an LPC vector obtained by carrying
out an LPC analysis on a speech signal in a processing
frame section corresponds to the quantization target)
is supplied to a distortion calculation section 21. Next,
the distortion calculation section 21 calculates a
Euclidean distortion between an LPC codevector stored
in the LPC codebook 22 and the quantization target vector
according to the following Expression (1):

where in Expression (1) , XT is a quantization target vector,
Cm is an mth (l