Linear predictive analysis apparatus, method, program and recording medium

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−i) i sample before the input time series signal Xo(n) or an input time series signal X o (n+i) 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 for each corresponding i,

wherein the linear predictive analysis method further comprises a coefficient determining step of acquiring the coefficient from one coefficient table among coefficient tables t 0 , t 1 and t 2 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 t 0 , a coefficient w t1 (i) is stored in the coefficient table t 1 , and a coefficient w t2 (i) is stored in the coefficient table t 2 ,

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 t 0 , 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 t 1 , 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 t 2 , for at least part of i other than i=0, w t0 (i)<w t1 (i)≤w t2 (i), for at least part of each i among other i other than i=0, w t0 (i)≤w t1 (i)<w t2 (i), and for the remaining each i other than i=0, w t0 (i)≤w t1 (i)≤w t2 (i).

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−i) i sample before the input time series signal X o (n) or an input time series signal X(n+i) 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 for each corresponding i,

wherein the linear predictive analysis method further comprises a coefficient determining step of acquiring the coefficient from at least one of coefficient tables t 0 and t 2 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 t 0 and a coefficient w t2 (i) is stored in the coefficient table t 2 ,

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 t 0 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 t 2 , for at least part of i other than i=0, w t0 (i)<w t2 (i) and for the remaining each i other than i=0, w t0 (i)≤w t2 (i),

the coefficient determining step determines, when the intensity of the periodicity or the pitch gain is medium, for at least part of i other than i=0, a coefficient w o (i) which satisfies w o (i)=β′×w t0 (i)+(1−β′)×w t2 (i) (0≤β′≤1).

3. 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:

processing circuitry 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−i) i sample before the input time series signal X o (n) or an input time series signal X o (n+i) i sample after the input time series signal X o (n) for each of at least i=0, 1, . . . , P max ; and

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 for each corresponding i,

wherein the processing circuitry is further configured to acquire the coefficient from one coefficient table among coefficient tables t 0 , t 1 and t 2 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 t 0 , a coefficient w t1 (i) is stored in the coefficient table t 1 , and a coefficient w t2 (i) is stored in the coefficient table t 2 ,

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 by the processing circuitry when the intensity of the periodicity or the pitch gain is high is set as a coefficient table t 0 , a coefficient table from which a coefficient is acquired by the processing circuitry when the intensity of the periodicity or the pitch gain is medium is set as a coefficient table t 1 , and a coefficient table from which a coefficient is acquired by the processing circuitry when the intensity of the periodicity or the pitch gain is low is set as a coefficient table t 2 , for at least part of i other than i=0, w t0 (i)<w t1 (i)≤w t2 (i), for at least part of each i among other i other than i=0, w t0 (i)≤w t1 (i)<w t2 (i), and for the remaining each i other than i=0, 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:

processing circuitry 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−i) i sample before the input time series signal X o (n) or an input time series signal X(n+i) i sample after the input time series signal X o (n) for each of at least i=0, 1, . . . , P max ; and

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 for each corresponding i,

wherein the processing circuitry is further configured to acquire the coefficient from at least one of coefficient tables t 0 and t 2 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 t 0 and a coefficient w t2 (i) is stored in the coefficient table t 2 ; and

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 by the processing circuitry when the intensity of the periodicity or the pitch gain is high is set as a coefficient table t 0 and a coefficient table from which a coefficient is acquired by the processing circuitry when the intensity of the periodicity or the pitch gain is low is set as a coefficient table t 2 , for at least part of i other than i=0, w t0 (i)<w t2 (i) and for the remaining each i other than i=0, w t0 (i)≤w t2 (i),

the processing circuitry determines, when the intensity of the periodicity or the pitch gain is medium, for at least part of i other than i=0, a coefficient w o (i) which satisfies w o (i)=β′×w t0 (i)+(1−β′)×w t2 (i) (0≤β′≤1).

5. A non-transitory computer readable recording medium in which a program causing a computer to execute each step of the linear predictive analysis method according to claim 1 or 2 is recorded.