Linear prediction analysis device, method, program, and storage medium
View Patent ↗An autocorrelation calculation unit 21 calculates an autocorrelation R O (i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′ O (i) obtained by multiplying a coefficient w O (i) by the autocorrelation R O (i). It is assumed here, for each order i of some orders i at least, that the coefficient w O (i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.
1. A linear prediction analysis method of obtaining, in each frame, which is a predetermined time interval, coefficients to be transformed to linear prediction coefficients corresponding to an input time-series signal, the linear prediction analysis method comprising:
a step of receiving the input time-series signal, the time-series signal being a speech signal or an acoustic signal;
an autocorrelation calculation step of calculating an 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 samples before the input time-series signal X O (n) or an input time-series signal X O (n+i) i samples after the input time-series signal X O (n), for each i of i=0, 1, . . . , P max at least, where the current frame includes parts of adjacent frame; and
a prediction coefficient calculation step of calculating coefficients to be transformed to first-order to P max -order linear prediction coefficients, by using a modified autocorrelation R′ O (i) obtained by multiplying a coefficient w O (i) by the autocorrelation R O (i) for each i,
wherein a coefficient table t0 stores a coefficient w t0 (i) and a coefficient table t1 stores a coefficient w t1 (i), w t0 (i)<w t1 (i) being satisfied for at least part of i other than i=0, w t0 (i)≤w t1 (i) being satisfied for the remaining each i other than i=0,
the linear prediction analysis method further comprises a coefficient determination step of, by using a period, a quantized value of the period, an estimated value of the period or a value that is negatively correlated with a fundamental frequency based on the input time-series signal of the current frame or a past frame,
(1) obtaining the coefficient w t0 (i) as the coefficient w O (i) from the coefficient table t0 when the period, the quantized value of the period, the estimated value of the period or the value that is negatively correlated with the fundamental frequency is less than or equal to a predetermined threshold or less than the predetermined threshold, and
(2) obtaining the coefficient w t1 (i) as the coefficient w O (i) from the coefficient table t1 when the period, the quantized value of the period, the estimated value of the period or the value that is negatively correlated with the fundamental frequency is more than the predetermined threshold or more than or equal to the predetermined threshold, and
the linear prediction analysis method further includes encoding or analyzing the speech signal or the acoustic signal using the calculated coefficients to be transformed to first order to Pmax-order linear prediction coefficients.
2. A linear prediction analysis method of obtaining, in each frame, which is a predetermined time interval, coefficients to be transformed to linear prediction coefficients corresponding to an input time-series signal, the linear prediction analysis method comprising:
a step of receiving the input time-series signal, the time-series signal being a speech signal or an acoustic signal;
an autocorrelation calculation step of calculating an 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 samples before the input time-series signal X O (n) or an input time-series signal X O (n+i) i samples after the input time-series signal X O (n), for each i of i=0, 1, . . . , Pmax at least, where the current frame includes parts of adjacent frame; and
a prediction coefficient calculation step of calculating coefficients to be transformed to first-order to P max -order linear prediction coefficients, by using a modified autocorrelation R′ O (i) obtained by multiplying a coefficient w O (i) by the autocorrelation R O (i) for each i;
wherein a coefficient table t0 stores a coefficient w t0 (i) and a coefficient table t1 stores a coefficient w t1 (i), w t0 (i)<w t1 (i) being satisfied for at least part of i other than i=0, w t0 (i)≤w t1 (i) being satisfied for the remaining each i other than i=0,
the linear prediction analysis method further comprises a coefficient determination step of, by using a fundamental frequency, a quantized value of the fundamental frequency, an estimated value of the fundamental frequency or a value that is positively correlated with a fundamental frequency based on the input time-series signal of the current frame or a past frame,
(1) obtaining the coefficient w t0 (i) as the coefficient w O (i) from the coefficient table t0 when the fundamental frequency, the quantized value of the fundamental frequency, the estimated value of the fundamental frequency or the value that is positively correlated with the fundamental frequency is more than or equal to a predetermined threshold or more than the predetermined threshold, and
(2) obtaining the coefficient w t1 (i) as the coefficient w O (i) from the coefficient table t1 when the fundamental frequency, the quantized value of the fundamental frequency, the estimated value of the fundamental frequency or the value that is positively correlated with the fundamental frequency is less than the predetermined threshold or less than or equal to the predetermined threshold, and
the linear prediction analysis method further includes encoding or analyzing the speech signal or the acoustic signal using the calculated coefficients to be transformed to first order to P max -order linear prediction coefficients.
3. A linear prediction analysis device that obtains, in each frame, which is a predetermined time interval, coefficients to be transformed to linear prediction coefficients corresponding to an input time-series signal, the linear prediction analysis device comprising:
processing circuitry configured to
receive the input time-series signal, the time-series signal being a speech signal or an acoustic signal;
calculate an 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 samples before the input time-series signal X O (n) or an input time-series signal X O (n+i) i samples after the input time-series signal X O (n), for each i of i=0, 1 . . . , P max at least, where the current frame includes parts of adjacent frame; and
calculate coefficients to be transformed to first-order to P max -order linear prediction coefficients, by using a modified autocorrelation R′ O (i) obtained by multiplying a coefficient w O (i) by the autocorrelation R O (i) for each i;
wherein a coefficient table t0 stores a coefficient w t0 (i) and a coefficient table t1 stores a coefficient w t1 (i), w t0 (i)<w t1 (i) being satisfied for at least part of i other than i=0, w t0 (i)≤w t1 (i) being satisfied for the remaining each i other than i=0,
the processing circuitry is further configured to, by using a period, a quantized value of the period, an estimated value of the period or a value that is negatively correlated with a fundamental frequency based on the input time-series signal of the current frame or a past frame,
(1) obtain the coefficient w t0 (i) as the coefficient w O (i) from the coefficient table t0 when the period, the quantized value of the period, the estimated value of the period or the value that is negatively correlated with the fundamental frequency is less than or equal to a predetermined threshold or less than the predetermined threshold, and
(2) obtain the coefficient w t1 (i) as the coefficient w O (i) from the coefficient table t1 when the period, the quantized value of the period, the estimated value of the period or the value that is negatively correlated with the fundamental frequency is more than the predetermined threshold or more than or equal to the predetermined threshold, and
the processing circuitry is configured to encode or analyze the speech signal or the acoustic signal using the calculated coefficients to be transformed to first order to Pmax-order linear prediction coefficients.
4. A linear prediction analysis device that obtains, in each frame, which is a predetermined time interval, coefficients to be transformed to linear prediction coefficients corresponding to an input time-series signal, the linear prediction analysis device comprising:
processing circuitry configured to
receive the input time-series signal, the time-series signal being a speech signal or an acoustic signal;
calculate an 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 samples before the input time-series signal X O (n) or an input time-series signal X O (n+i) i samples after the input time-series signal X O (n), for each i of i=0, 1, . . . , Pmax at least, where the current frame includes parts of adjacent frame; and
calculate coefficients to be transformed to first-order to P max -order linear prediction coefficients, by using a modified autocorrelation R′O(i) obtained by multiplying a coefficient w O (i) by the autocorrelation RO(i) for each i;
wherein a coefficient table t0 stores a coefficient w t0 (i) and a coefficient table t1 stores a coefficient w t1 (i), wt0(i)<w t1 (i) being satisfied for at least part of i other than i=0, w t0 (i)≤w t1 (i) being satisfied for the remaining each i other than i=0,
the processing circuitry is further configured to, by using a fundamental frequency, a quantized value of the fundamental frequency, an estimated value of the fundamental frequency or a value that is positively correlated with a fundamental frequency based on the input time-series signal of the current frame or a past frame,
(1) obtain the coefficient w t0 (i) as the coefficient w O (i) from the coefficient table t0 when the fundamental frequency, the quantized value of the fundamental frequency, the estimated value of the fundamental frequency or the value that is positively correlated with the fundamental frequency is more than or equal to a predetermined threshold or more than the predetermined threshold, and
(2) obtain the coefficient w t0 (i) as the coefficient w O (i) from the coefficient table t1 when the fundamental frequency, the quantized value of the fundamental frequency, the estimated value of the fundamental frequency or the value that is positively correlated with the fundamental frequency is less than the predetermined threshold or less than or equal to the predetermined threshold, and
the processing circuitry is configured to encode or analyze the speech signal or the acoustic signal using the calculated coefficients to be transformed to first order to P max -order linear prediction coefficients.
5. A non-transitory computer readable medium that stores a program for causing a computer to execute a linear prediction analysis method of obtaining, in each frame, which is a predetermined time interval, coefficients to be transformed to linear prediction coefficients corresponding to an input time-series signal, the linear prediction analysis method comprising:
a step of receiving the input time-series signal, the time-series signal being a speech signal or an acoustic signal;
an autocorrelation calculation step of calculating an 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 samples before the input time-series signal X O (n) or an input time-series signal X O (n+i) i samples after the input time-series signal X O (n), for each i of i=0, 1, . . . , P max at least, where the current frame includes parts of adjacent frame; and
a prediction coefficient calculation step of calculating coefficients to be transformed to first-order to P max -order linear prediction coefficients, by using a modified autocorrelation R′ O (i) obtained by multiplying a coefficient w O (i) by the autocorrelation R O (i) for each i,
wherein a coefficient table t0 stores a coefficient w t0 (i) and a coefficient table t1 stores a coefficient w t1 (i), w t0 (i)<w t1 (i) being satisfied for at least part of i other than i=0, w t0 (i)≤w t1 (i) being satisfied for the remaining each i other than i=0,
the linear prediction analysis method further comprises a coefficient determination step of, by using a period, a quantized value of the period, an estimated value of the period or a value that is negatively correlated with a fundamental frequency based on the input time-series signal of the current frame or a past frame,
(1) obtaining the coefficient w t0 (i) as the coefficient w O (i) from the coefficient table t0 when the period, the quantized value of the period, the estimated value of the period or the value that is negatively correlated with the fundamental frequency is less than or equal to a predetermined threshold or less than the predetermined threshold, and
(2) obtaining the coefficient w t1 (i) as the coefficient w O (i) from the coefficient table t1 when the period, the quantized value of the period, the estimated value of the period or the value that is negatively correlated with the fundamental frequency is more than the predetermined threshold or more than or equal to the predetermined threshold, and
the linear prediction analysis method further includes encoding or analyzing the speech signal or the acoustic signal using the calculated coefficients to be transformed to first order to Pmax-order linear prediction coefficients.
6. A non-transitory computer readable medium that stores a program for causing a computer to execute a linear prediction analysis method of obtaining, in each frame, which is a predetermined time interval, coefficients to be transformed to linear prediction coefficients corresponding to an input time-series signal, the linear prediction analysis method comprising:
a step of receiving the input time-series signal, the time-series signal being a speech signal or an acoustic signal;
an autocorrelation calculation step of calculating an 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 samples before the input time-series signal X O (n) or an input time-series signal X O (n+i) i samples after the input time-series signal X O (n), for each i of i=0, 1, . . . , Pmax at least, where the current frame includes parts of adjacent frame; and
a prediction coefficient calculation step of calculating coefficients to be transformed to first-order to P max -order linear prediction coefficients, by using a modified autocorrelation R′O(i) obtained by multiplying a coefficient w O (i) by the autocorrelation RO(i) for each i;
wherein a coefficient table t0 stores a coefficient w t0 (i) and a coefficient table t1 stores a coefficient w t1 (i), wt0(i)<w t1 (i) being satisfied for at least part of i other than i=0, w t0 (i)≤w t1 (i) being satisfied for the remaining each i other than i=0,
the linear prediction analysis method further comprises a coefficient determination step of, by using a fundamental frequency, a quantized value of the fundamental frequency, an estimated value of the fundamental frequency or a value that is positively correlated with a fundamental frequency based on the input time-series signal of the current frame or a past frame,
(1) obtaining the coefficient w t0 (i) as the coefficient w O (i) from the coefficient table t0 when the fundamental frequency, the quantized value of the fundamental frequency, the estimated value of the fundamental frequency or the value that is positively correlated with the fundamental frequency is more than or equal to a predetermined threshold or more than the predetermined threshold, and
(2) obtaining the coefficient w t1 (i) as the coefficient w O (i) from the coefficient table t1 when the fundamental frequency, the quantized value of the fundamental frequency, the estimated value of the fundamental frequency or the value that is positively correlated with the fundamental frequency is less than the predetermined threshold or less than or equal to the predetermined threshold, and
the linear prediction analysis method further includes encoding or analyzing the speech signal or the acoustic signal using the calculated coefficients to be transformed to first order to P max -order linear prediction coefficients.