Linear predictive analysis apparatus, method, program and recording medium

Patent metadata

Field	Value
Publication number	US-2016336019-A1
Application number	US-201515112534-A
Country	US
Kind code	A1
Filing date	Jan 20, 2015
Priority date	Jan 24, 2014
Publication date	Nov 17, 2016
Grant date	—

Abstract

An autocorrelation calculating part calculates autocorrelation R o (i) from an input signal. A predictive coefficient calculating part performs linear predictive analysis using modified autocorrelation R′ o (i) obtained by multiplying the autocorrelation R o (i) by a coefficient w o (i). Here, a case is comprised where, for at least part of each order i, the coefficient w o (i) corresponding to each order i monotonically decreases as a value having positive correlation with a pitch gain in an input signal of a current frame or a past frame increases.

First claim

1 : A linear predictive analysis method for obtaining a coefficient which can be converted into a linear predictive coefficient corresponding to an input time series signal for each frame which is a predetermined time interval, the linear predictive analysis method comprising:
an autocorrelation calculating step of calculating autocorrelation R o (i) between an input time series signal X o (n) of a current frame and an input time series signal X o (n−1) i sample before the input time series signal X o (n) or an input time series signal X o (n+1) i sample after the input time series signal X o (n) for each of at least i=0, 1, . . . , P max ; and a predictive coefficient calculating step of obtaining a coefficient which can be converted into linear predictive coefficients from the first-order to the P max -order using modified autocorrelation R′ o (i) obtained by multiplying the autocorrelation R o (i) by a coefficient w o (i) for each corresponding i, wherein a case is comprised where, for at least part of each order i, the coefficient w o (i) corresponding to each order i monotonically decreases as a value having positive correlation with intensity of periodicity of an input time series signal of the current frame or a past frame or a pitch gain based on the input time series signal increases.

2 : A linear predictive analysis method for obtaining a coefficient which can be converted into a linear predictive coefficient corresponding to an input time series signal for each frame which is a predetermined time interval, the linear predictive analysis method comprising:
an autocorrelation calculating step of calculating autocorrelation R o (i) between an input time series signal X o (n) of a current frame and an input time series signal X o (n−1) i sample before the input time series signal X o (n) or an input time series signal X o (n+1) i sample after the input time series signal X o (n) for each of at least i=0, 1, . . . , P max ; and a coefficient determining step of acquiring a coefficient w o (i) from one coefficient table among two or more coefficient tables using a value having positive correlation with intensity of periodicity of an input time series signal of the current frame or a past frame or a pitch gain based on the input time series signal assuming that each order i where i=0, 1, . . . , P max and the coefficient w o (i) corresponding to the each order i are stored in association with each other in each of the two or more coefficient tables; and a predictive coefficient calculating step of obtaining a coefficient which can be converted into linear predictive coefficients from the first-order to the P max -order using modified autocorrelation R′ o (i) obtained by multiplying the autocorrelation R o (i) by the acquired coefficient w o (i) for each corresponding i, wherein, among the two or more coefficient tables, a coefficient table from which the coefficient w o (i) is acquired in the coefficient determining step when the value having positive correlation with the intensity of the periodicity or the pitch gain is a first value is set as a first coefficient table, among the two or more coefficient tables, a coefficient table from which the coefficient w o (i) is acquired in the coefficient determining step when the value having positive correlation with the intensity of the periodicity or the pitch gain is a second value which is smaller than the first value is set as a second coefficient table, and for at least part of each order i, a coefficient corresponding to the each order i in the second coefficient table is greater than a coefficient corresponding to the each order i in the first coefficient table.

3 : A linear predictive analysis method for obtaining a coefficient which can be converted into a linear predictive coefficient corresponding to an input time series signal for each frame which is a predetermined time interval, the linear predictive analysis method comprising:
an autocorrelation calculating step of calculating autocorrelation R o (i) between an input time series signal X o (n) of a current frame and an input time series signal X o (n−1) i sample before the input time series signal X o (n) or an input time series signal X o (n+1) i sample after the input time series signal X o (n) for each of at least i=0, 1, . . . , P max ; a coefficient determining step of acquiring a coefficient from one coefficient table among coefficient tables t0, t1 and t2 using a value having positive correlation with intensity of periodicity of an input time series signal of the current frame or a past frame or a pitch gain based on the input time series signal assuming that a coefficient w t0 (i) is stored in the coefficient table t0, a coefficient w t1 (i) is stored in the coefficient table t1, and a coefficient w t2 (i) is stored in the coefficient table t2; and a predictive coefficient calculating step of obtaining a coefficient which can be converted into linear predictive coefficients from the first-order to the P max -order using modified autocorrelation R′ o (i) obtained by multiplying the autocorrelation R o (i) by the acquired coefficient for each corresponding i, wherein, assuming that, according to the value having positive correlation with the intensity of the periodicity or the pitch gain, a case is classified into any of a case where the intensity of the periodicity or the pitch gain is high, a case where the intensity of the periodicity or the pitch gain is medium, and a case where the intensity of the periodicity or the pitch gain is low, a coefficient table from which a coefficient is acquired in the coefficient determining step when the intensity of the periodicity or the pitch gain is high is set as a coefficient table t0, a coefficient table from which a coefficient is acquired in the coefficient determining step when the intensity of the periodicity or the pitch gain is medium is set as a coefficient table t1, and a coefficient table from which a coefficient is acquired in the coefficient determining step when the intensity of the periodicity or the pitch gain is low is set as a coefficient table t2, for at least part of i, w t0 (i)≦w t1 (i)≦w t2 (i), for at least part of each i among other i, w t0 (i)≦w t1 (i)<w t2 (i), and for the remaining each i, w t0 (i)<w t1 (i)≦w t2 (i).

4 : A linear predictive analysis apparatus which obtains a coefficient which can be converted into a linear predictive coefficient corresponding to an input time series signal for each frame which is a predetermined time interval, the linear predictive analysis apparatus comprising:
an autocorrelation calculating part configured to calculate autocorrelation R o (i) between an input time series signal X o (n) of a current frame and an input time series signal X o (n−1) i sample before the input time series signal X o (n) or an input time series signal X o (n+1) i sample after the input time series signal)(an) for each of at least i=0, 1, . . . , P max ; and a predictive coefficient calculating part configured to obtain a coefficient which can be converted into linear predictive coefficients from the first-order to the P max -order using modified autocorrelation R′ o (i) obtained by multiplying the autocorrelation R o (i) by a coefficient w o (i) for each corresponding i, wherein a case will be comprised where, for at least part of each order i, the coefficient w o (i) corresponding to the each order i monotonically decreases as a value having positive correlation with intensity of periodicity of an input time series signal of the current frame or a past frame or a pitch gain based on the input time series signal increases.

5 : A linear predictive analysis apparatus which obtains a coefficient which can be converted into a linear predictive coefficient corresponding to an input time series signal for each frame which is a pred

Assignees

• Nippon Telegraph & Telephone

Inventors

• Kamamoto Yutaka
• Moriya Takehiro
• Harada Noboru

Classifications

• G10L25/12

  the extracted parameters being prediction coefficients · CPC title

• G10L19/06 Primary

  Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients · CPC title

• G10L25/21

  the extracted parameters being power information · CPC title

• G10L25/90

  Pitch determination of speech signals · CPC title

• G10L25/06 Primary

  the extracted parameters being correlation coefficients · CPC title