Patent Translate Powered by EPO and Google Notice This translation is machine-generated. It cannot be guaranteed that it is intelligible, accurate, complete, reliable or fit for specific purposes. Critical decisions, such as commercially relevant or financial decisions, should not be based on machine-translation output. DESCRIPTION JP2009037032 An object signal is extracted effectively. SOLUTION: A signal from a specific signal source is emitted from an observation signal R (p) of a B channel obtained by observing a mixed signal of signals emitted from a plurality of signal sources by B (B ≧ 2) sensors. The noise signal N (p) contained in the observation signal R (p) is detected (120, S4), and the noise signal N (p) and the observation signal R (p) are extracted. The whitening filter coefficient w (p) is calculated (112, S6), and the noise correlation function which is the inter-channel correlation function of the noise signal N (p) included in the observed signal of B channel is calculated (130, S14) , And B: Calculate the observed correlation function which is the correlation function between the observed signals of the B channel (4, S10), and obtain the inverse filter coefficient c (p) using the observed correlation function and the noise correlation function (140, S16 ), The inverse filter coefficient to the observation signal R (p) Crowded only adds (3, S20). [Selected figure] Figure 9 Signal extraction apparatus, method thereof, and program thereof [0001] The present invention relates to a signal extraction apparatus, a method thereof, and a program thereof for well extracting a target signal from mixed signals emitted from a plurality of signal sources. [0002] In recent years, with the advancement of multimedia technology, communication conferences such as video conferences in the form of a speech communication using a sensor (for example, a 04-05-2019 1 microphone) and a speaker have become widespread. In that case, natural speech can be made without being aware of the microphones without placing microphones for the number of speakers on the desk, and noise and reverberation that degrades the voice quality can be suppressed, and a signal for observing only the target voice signal An extractor is needed. [0003] As such prior art, there is a signal extraction device that suppresses reverberation and noise by using a plurality of microphones. The details of this signal extraction device are described in Patent Document 1. FIG. 1 shows an example of the functional configuration of a signal extraction apparatus 500 according to the prior art. The number of signal sources is A, and among them, the number of sound sources of the target signal (target sound sources) is one, and the number of sound sources of noise signals (noise source) is A−1. Let the number of microphones be B. However, A and B are integers of 2 or more. B microphones 1 b (b = 1,..., B) are connected to the signal extraction device 500. The signal extraction device 500 includes a whitening filter coefficient calculation unit 110, an observed correlation function calculation unit 4, an inverse filter coefficient calculation unit 5, B whitening filter units 100b (b = 1,..., B), B pieces The filter unit 2 b (b = 1,..., B) of FIG. Also, using the discrete time p, the target signal is M (p), the noise signal is N (p), and the observation signal Rb (p) observed by the microphone 1b. The observation signal Rb (p) is a signal in which the target signal Mb (p) and the noise signal Nb (p) are mixed. [0004] First, the observation signal Rb (p) from the microphone 1b is input to the whitening filter coefficient calculation unit 110 and the filter unit 2b. The whitening filter coefficient calculation unit 110 estimates the average spectrum of the target signal M (p) from the observation signal Rb (p). Then, the coefficients of the whitening filter are calculated to flatten the average spectrum. The calculation of the whitening filter coefficients is performed as follows. First, the signal R1 (p) of the channel (for example, channel 1) specified in advance from the observation signal Rb (p) is divided into F frames. Let the frame length of all the frames be G (for example, 256). Then, the autocorrelation function U1f (p) of the f (f = 1,..., F) -th frame is calculated by the following equation. U1f (p) =. SIGMA.qR1 (p) R1 (p + q) (1) where q = G.times. (F-1), G.times. (F-1) +1,. . . , G × f−2, G × f−1 p = −G,. . . , 0,. . . , G. This is averaged for a frame, and an average 04-05-2019 2 autocorrelation function U <-> 1 (p) is calculated by the following equation. U <-> 1 (p) =. SIGMA.f = 1 <F> U1f (p) (2) [0005] By converting the average autocorrelation function U <-> 1 (p) into the frequency domain, that is, the following equation (3), the average spectrum V1 (k) of the observed signal can be obtained. For example, Fourier transform may be used as the conversion method to the frequency domain. V1 (k) = FFT (U <-> 1 (p)) (3) However, FFT (U <-> 1 (p)) is Fourier-transformed with respect to U <-> 1 (p) Yes, k indicates the frequency. [0006] As another method of calculating the average spectrum, there is also a method of calculating the spectrum of the signal R1 (p) for each frame and taking an average for the frame as shown in the following equation (4). V1 (k) =. SIGMA.f = 1 <F> | FFT (u1 (p)) | (4) [0007] Next, the spectrum W1 (k) of the whitening filter is obtained by calculating the reciprocal of the calculated average spectrum as in the following equation (5). W1 (k) = 1 / V1 (k) (5) The filter coefficient w1 (p) of the whitening filter performs an inverse Fourier transform IFFT on this spectrum W1 (k) as in the following equation (6) to obtain a window It will be calculated separately. There are Hanning window, Hamming window, square window, triangular window, Kaiser window etc as window types. w1 (p) = window (IFFT (W1 (k))) (6) These processes are performed for each channel b (b = 1,..., B) to obtain a whitening filter coefficient wb (p). [0008] Next, the whitening filter unit 100b (b = 1,..., B) convolves the whitening filter coefficient wb (p) with the observation signal Rb (p) as shown in the following equation (7). , Whitening the observation signal Rb (p). Hereinafter, the whitened observation signal is referred to as a whitened observation signal. The observation correlation function calculator 4 calculates the 04-05-2019 3 correlation function r'11 (p), r'12 (p),... Between the whitened observation signals R'n (p) (n = 1,..., N). . . , R'1B (p), r'21 (p),. . . , R'2B (p),. . . , R'B1 (p),. . . , R ′ BB (p). Hereinafter, this correlation function is represented as r 'ij (p) (i = 1, ..., B, j = 1, ..., B). Here, r'ij (p) =. SIGMA.qR'i (q) R'j (q + p) (8). [0009] The inverse filter coefficient calculation unit 5 calculates an inverse filter by solving the following simultaneous linear equations. The inverse filter coefficient is capable of suppressing the noise signal and extracting the target signal by convoluting the observation signal in which the target signal and the noise signal are mixed. [0010] Here, R is a matrix of interchannel correlation functions, R ij is a matrix of correlation functions of the i-th microphone 1i and the j-th microphone 1j, c is a vector of inverse filter coefficients to be obtained, cb is the n-th inverse filter coefficients , D is the blind objective impulse response coefficient vector, db is the b-th blind objective impulse response vector, c b (L) is the b-th inverse filter coefficient, B is the number of microphones, and L is the number of taps of the inverse filter is there. δ b is “1” when the target sound source 61 is closest to the b-th microphone 1 b among the microphones, and “0” otherwise. [0011] The simultaneous linear equations of equation (9) are solved, and the inverse filter coefficient vector c is calculated to obtain the inverse filter coefficient cb (p). In order to solve the simultaneous linear equations of Equation (4), the same conditions as the MINT theory (the following Equations (10) and (11) must be satisfied. B> A + 1 (10) L = A (K-1) / (N−A) (11) where A is the number of sound sources and K is the number of impulse response taps. For MINT theory, see "M. Miyoshi and Y. Kaneda," Invese Filtering of acoustics, "IEEE Trans Acoust. Speech Signal Process., Vol. ASSP-36, no 2, pp. 145-152, Feb. 1998. "It is described in. [0012] 04-05-2019 4 Each filter unit 2b obtains the signal Sb (p) by convolving the inverse filter coefficient cb (p) with the observation signal Rb (p). The output Sb (p) of each filter unit 2b is all added by the adder 3, and the addition result is output as the target signal S (p). The output signal S (p) suppresses noise and reverberation and extracts only the target sound. Unexamined-Japanese-Patent No. 2006-66989 [0013] When implementing the conventional signal extraction apparatus 500, it had to be implemented in the environment where the microphone closest to the target sound source and the microphone closest to the noise source exist separately. Under this environment, in the simultaneous linear equations of the above equation (9), the number of equations was insufficient, and it was not possible to obtain an appropriate inverse filter coefficient. Therefore, there is a problem that although the reverberation of the speech to be extracted is suppressed, the noise is not removed. [0014] An object of the present invention is to provide a signal extraction device, its method, and its program for effectively removing reverberation and noise of a target signal by obtaining an inverse filter coefficient in consideration of a noise signal. [0015] The present invention is generated from a specific signal source from B-channel observation signals obtained by respectively observing mixed signals of signals respectively emitted from a plurality of signal sources with B (B ≧ 2) sensors. It is a signal extraction device that extracts a target signal. The signal extraction apparatus includes a noise correlation function calculation unit, an observation correlation function calculation unit, a weighted inverse filter coefficient calculation unit, a filter unit, and an addition unit. The noise correlation function calculation unit calculates a noise correlation function which is an inter-channel correlation function of the noise signal included in each of the B channel observation signals. The observation correlation function calculation unit calculates an observation correlation function which is an inter-channel correlation function of the B-channel observation signal. The weighted inverse filter coefficient 04-05-2019 5 calculation unit obtains the inverse filter coefficient using the observation correlation function and the noise correlation function. The filter unit convolves inverse filter coefficients into the observation signal. The addition unit generates the target signal by adding the output of the filter unit. [0016] Furthermore, it may have a whitening filter coefficient calculation unit and a whitening filter unit. The whitening filter coefficient calculation unit calculates the whitening filter coefficient using the average spectrum of the observation signal. The whitening filter unit convolves a whitening filter coefficient to the input signal to whiten the signal. In this case, the observation correlation function calculation unit calculates an inter-channel correlation function of the B-channel observation signal whitened by the whitening filter unit, and outputs it as an observation correlation function. [0017] Furthermore, the noise correlation function calculation unit may calculate an inter-channel correlation function of the noise signal whitened by the whitening filter unit and output it as a noise correlation function. [0018] Furthermore, a noise section detection unit that detects a noise signal included in the observation signal may be provided. In this case, the noise correlation function calculator calculates a noise correlation function between the detected noise signals. Furthermore, in this case, the whitening filter coefficient calculation unit may calculate the whitening filter coefficient using the average spectrum of the noise signal detected by the noise section detection unit and the average spectrum of the B channel observation signal. [0019] 04-05-2019 6 Further, the signal extraction apparatus of the present invention may include a noise section detection unit, a noise correlation function calculation unit, an observation correlation function calculation unit, a weighted inverse filter coefficient calculation unit, a filter unit, and an addition unit. The noise section detection unit detects noise section information including only a noise signal from the observation signal of the B channel. The noise correlation function calculation unit calculates a noise correlation function, which is an inter-channel correlation function of noise signals respectively included in the B channel observation signal, from the B channel observation signal and the noise section information. The observation correlation function calculation unit, the weighted inverse filter coefficient calculation unit, the filter unit, and the addition unit are the same as described above. [0020] Furthermore, in the case of the configuration of this signal extraction device, a whitening filter coefficient calculation unit and a whitening filter unit may be provided. The whitening filter coefficient calculation unit calculates a whitening filter coefficient using the average spectrum of the B-channel observed signal. The whitening filter unit convolutes the whitening filter coefficients to the input signal. Then, the observation correlation function calculation unit calculates a correlation function between channels of the observation signal of the B channel whitened by the whitening filter unit, and outputs the correlation function as the observation correlation function. The noise correlation function calculation unit calculates a noise correlation function from the whitened observation signal of the B channel and the noise interval information. [0021] In this case, the noise section detection unit may also detect a noise signal. Then, the whitening filter coefficient calculation unit calculates a whitening filter coefficient using the detected average spectrum of the noise signal and the average spectrum of the B channel observation signal. [0022] According to the above configuration, an observed correlation function which is a correlation function between channels of the observation signal and a noise correlation function which is a 04-05-2019 7 correlation function between channels of the noise signal are obtained. The observed correlation function is then constrained by the noise correlation function to obtain the inverse filter coefficients. That is, the equation reflecting the characteristics of the noise signal increases. As a result, even if the microphone closest to the noise source and the microphone closest to the target sound source are the same, not only the reverberation of the target signal but also noise can be effectively suppressed. [0023] The following shows the best mode for carrying out the invention. In addition, the same number is attached | subjected to the structure part which has the same function, and the process which performs the same process, and it abbreviate | omits duplication description. [0024] In the description of the signal extraction apparatus of this embodiment, although the signal to be extracted is described as an audio signal, the present invention is not limited to this. According to the signal extraction apparatus of this embodiment, for example, it is possible to clearly extract the target electromagnetic wave from the electromagnetic wave buried in the noise. Further, a sensor that observes a signal is, for example, a microphone. FIG. 2 shows an example of the functional configuration of the signal extraction device 600-1 of the first embodiment, and FIG. 3 shows the flow of the main processing of the signal extraction device 600-1. As described in [Background Art], the number of signal sources is A, and among them, the number of sound sources of the target signal is one, and the number of sound sources of the noise signal is A-1. Let the number of microphones be B. However, A and B are integers of 2 or more. B microphones 1 b (b = 1,..., B) are connected to the signal extraction device 600-1. The signal extraction device 600-1 includes a noise section detection unit 120, a whitening filter coefficient calculation unit 110, a whitening filter unit 100b (b = 1,..., B), and a filter unit 2b (b = 1,. , B), observation correlation function calculator 4, noise correlation function calculator 130, and weighted inverse filter coefficient calculator 140. [0025] First, when the observation signal Rb (p) is observed by the microphone 1 b (step S 2), the observation signal Rb (p) is input to the noise section detection unit 120. The noise section 04-05-2019 8 detection unit 120 detects the noise signal Nb (p) included in the observation signal Rb (p) (step S4). For example, the target signal Mb (p) is considered as voice, and a signal outside the voice section is output as a noise signal Nb (p) using a general VAD (voice detection technology: voice activity detection). [0026] Further, the whitening filter coefficient calculation unit 110 calculates a whitening filter coefficient using the average spectrum of the observation signal Rb (p) from the microphone 1b (step S6). The average spectrum and the whitening filter coefficient may be obtained by using the above formulas (1) to (6), and the description is omitted here. The whitening filter unit 100b convolutes the whitening filter coefficient wb (p) into the corresponding observation signal Rb (p) according to the above equation (7) to generate a whitened observation signal R′b (p) Step S8). From the above equation (8), the observed correlation number calculation unit 4 obtains the observed correlation function r'ij (i = 1,...) For the whitened observed signal R'b (p). , B j = 1,..., B) are obtained (step S10). [0027] On the other hand, the whitening filter unit 100b convolutes the whitening filter coefficient wb (p) corresponding to the noise signal Nb (p) to generate a whitened noise signal N'b (p) (step S12). The noise correlation function calculation unit 130 calculates noise correlation functions n ′ ij (p) (i = 1,..., B j = 1,...) Which are correlation functions between channels of the whitening noise signal N ′ b (p). .., B) are obtained (step S14). [0028] Here, n'ij (p) =. SIGMA.qN'i (q) N'j (q + p) (12). This addition process adds reverberation time for q. Although FIG. 2 separately shows the whitening filter portion obtained by whitening the observed signal Rb (p) and the whitening filter portion obtained by whitening the noise signal Nb (p), these may be used in combination. . The weighted inverse filter coefficient calculation unit 140 calculates the weighted inverse filter coefficient cb (p) using the noise correlation function N and the observation correlation function R. Specifically, the inverse filter cb (p) is obtained by solving the following simultaneous equations (13). 04-05-2019 9 [0029] Where λ 1 is a correction coefficient, R is a matrix of inter-channel correlation functions, R ij is a matrix of inter-channel correlation functions of i-th microphone 1 i and j-th microphone 1 j, c is a vector of inverse filter coefficients, c n is vector of n-th inverse filter coefficient, d is blind objective impulse response coefficient vector, dn is n-th blind objective impulse response vector, cb (L) is b-th inverse filter coefficient, B is number of microphones, L is It is the number of reverse filter taps. δ b is 1 when the target sound source 61 is closest to the b-th microphone 1 b among the microphones, and is 0 otherwise. The elements of the matrix d are BL in d1 to dB, and the number of "0" s is also BL. Therefore, the number of elements of the matrix d is 2BL. [0030] The equation (13) is shown in detail in FIG. Also, in order to solve equation (13), the MINT theory described in [Background Art] must hold. [0031] The weighted inverse filter coefficient cb (p) thus obtained is input to the corresponding filter unit 2b. The filter unit 2b convolutes the weighted inverse filter coefficient cb (p) with the observation signal Rb (p) to generate a signal M '(p). That is, the following equation (14) is performed. The adder 3 adds the generated signal M'b (p) for all channels to generate a target signal M (p). That is, it is obtained by the following equation. M (p) =. SIGMA.b = 1 <B> M'b (p) (15) [0032] Further, if the value of λ1 in the above equation (13) is made smaller, the above equation (9) will be approached, and the reverberation of the target signal can be suppressed, but the noise signal can not be suppressed. Although the noise signal can be suppressed by increasing the value of λ1, the reverberation of the target signal becomes large. Therefore, the value of λ1 may be changed as appropriate in consideration of the environment in which the target signal is extracted. In addition, although λ1 is applied to the matrix N of the noise correlation function in 04-05-2019 10 the matrix RE in the above equation (13), λ1 may be applied to the matrix R of the observed correlation function. In this case, if the value of λ1 is increased, the reverberation of the target signal can be suppressed, but the noise signal can not be suppressed. If the value of λ1 is reduced, the noise signal can be suppressed, but the reverberation of the target signal becomes large. Alternatively, λ1 may be applied to the matrix N of the observed correlation function, and a correction coefficient λ1 ′ different from λ1 may be applied to the matrix R. [0033] The observation signal Rb (p) includes the noise signal Nb (p) and the target signal Mb (p). Folding the whitening filter coefficient wb (p) into the observation signal Rb (p) means that the target signal Mb (p) in the observation signal can be appropriately whitened, but the noise signal Nb in the observation signal About (p), it has not been able to whiten appropriately. Therefore, the noise signal Nb (p) from the noise section detection unit 120 is also whitened with wb (p), and using the whitened observation signal and noise signal, an appropriate weighted inverse filter can be obtained. it can. [0034] When the matrix R used in obtaining the inverse matrix by the signal extraction device 500 is compared with the matrix RE used in the signal extraction device 600-1, the matrix N of the noise correlation function is added to the matrix RE. Therefore, the number of equations has conventionally been increased, and the characteristics of the noise signal Nb (p) are also taken into consideration. Therefore, even under an environment where the microphone closest to the noise source and the microphone closest to the target sound source are the same, it is possible to obtain an inverse matrix more suitable than before, and as a result, noise can be suppressed effectively. [0035] [Modification 1] Next, a signal extraction device 600-2 which is a modification 1 of the embodiment 1 will be described. An example of functional configuration of the signal extraction device 600-2 is shown in FIG. The signal extraction device 600-2 differs from the signal extraction device 600-1 in that the noise signal Nb (p) is not whitened. That is, the noise correlation function calculation unit 130 obtains the inter-channel correlation function of the 04-05-2019 11 noise signal Nb (p). For example, if the noise signal has a waveform close to the whitened signal, it is not necessary to whiten. Therefore, if the noise signal has a waveform close to the whitened signal, this configuration can omit the whitening process of the noise signal, and the effect similar to that of the signal extraction device 600-1. You can get [0036] [Modification 2] Next, a signal extraction device 600-3 which is a modification 2 of the embodiment 1 will be described. An exemplary functional configuration of the signal extraction device 600-3 is shown in FIG. The signal extraction device 600-3 differs from the signal extraction device 600-1 in that the noise segment detection unit 120 is not provided and the noise signal Nb (p) is not detected. Under circumstances where noise signals can be predicted to some extent, it may not be necessary to detect noise signals. For example, when it is desired to extract as a voice signal target signal of the speaker in the conference room, the noise signal is often air conditioning in the conference room and the like, and the noise signal can be predicted in advance. Therefore, if the noise signal to be predicted is input in advance and the noise correlation function for the input noise signal is calculated by the noise correlation function calculation unit 130, the noise correlation function is obtained without detecting the noise signal. , And the same effect as the signal extraction device 600-1 can be obtained. [0037] [Modification 3] Next, a signal extraction device 600-4 of Modification 3 will be described. An example of functional configuration of the signal extraction device 600-4 is shown in FIG. The signal extraction device 600-4 differs from the signal extraction device 600-1 in that the whitening filter unit 100b and the whitening filter coefficient calculation unit 110 are not provided. For example, in the case of a waveform close to a whitened signal for both the noise signal and the observation signal, it is not necessary to whiten the noise signal and the observation signal. Therefore, the calculation process of the whitening filter coefficient, the convolution process of the whitening coefficient to the noise signal, and the convolution process of the whitening coefficient to the observation signal can be omitted, and the same effect as the signal extraction device 600-1 can be obtained. [0038] 04-05-2019 12 [Modification 4] Next, a signal extraction device 600-5 of Modification 4 will be described. Signal extractor 600-5 is shown in FIG. The signal extraction device 600-5 differs from the signal extraction device 600-4 in that the noise segment detection unit 120 is not provided. As described in [Modification 2], if the noise signal can be predicted in advance and both the noise signal and the target signal have a waveform close to the whitened signal, such as the signal extraction device 600-5 By using the configuration, the amount of computation can be significantly reduced, and the same effect as that of the signal extraction device 600-1 can be obtained. [0039] In the configuration of the signal extraction device 600-1 described in the first embodiment, when the power of the noise signal Nb (p) is larger than the power of the target signal Mb (p), the whitening filter unit 100b affects the noise signal. As a result, the noise signal and the observation signal can not be appropriately whitened, which causes a problem that the dereverberation performance of the target signal and the noise suppression performance are degraded. Therefore, when calculating the whitening filter coefficients (step S6 in FIG. 3), the signal extraction device 600-6 according to the second embodiment uses the observation signal and the noise signal instead of using only the observation signal. , Whitening filter coefficients. The whitening filter coefficients thus obtained are more accurate than the whitening filter coefficients obtained in the first embodiment in whitening the target signal. [0040] The functional structural example of the signal extraction apparatus 600-6 of Example 2 is shown in FIG. Signal extraction apparatus 600-6 is different from signal extraction apparatus 600-1 in that observation signal Rb (p) and noise signal Nb (p) from noise section detection unit 120 are input to the whitening filter coefficient calculation unit. It is different. The reference number of this whitening filter coefficient calculation unit is 112. The whitening filter coefficient calculation unit 112 uses the average spectrum VR (k) of the observed signal Rb (p) and the average spectrum VN (k) of the noise signal Nb (p) to calculate the average spectrum of the target signal Mb (p). Estimate VM1 (k). Specifically, it is obtained by the following equation (16). VM1 (k) = VR (k) −λ2VN (k) (16) Here, λ2 is a correction coefficient, and 0 <λ2 <1. Although VN (k) is multiplied by the correction coefficient in Equation (16), VR (k) may be multiplied. Also, both VN (k) and VR (k) may be multiplied. Then, the whitening filter coefficient wMb (p) of the target signal can be obtained by calculating V1 (k) in equation (5) as VM1 (k) and using equation (6). Using the noise signal N'b (p) whitened with this wMb (p) and the observation signal R'b (p), 04-05-2019 13 the noise correlation function calculation unit 130 and the observation correlation function calculation unit 4 measure the noise correlation function and the observation correlation Find the function. [0041] The observation signal Rb (p) includes the noise signal Nb (p) and the target signal Mb (p). Convolving the whitening filter coefficient wMb of the target signal into the observation signal Rb (p) means that the target signal Mb (p) in the observation signal can be appropriately whitened, but the noise signal Nb in the observation signal About (p), it has not been able to whiten appropriately. Therefore, the noise signal Nb (p) from the noise section detection unit 120 is also whitened with wMb, and by using the whitened observation signal and noise signal, an appropriate weighted inverse filter can be obtained. [0042] In the signal extraction device 600-1 of the first embodiment, since the whitening filter is calculated including the characteristics of the noise signal Nb (p), the whitening of the target signal has not been achieved. However, by subtracting the average spectrum VN (k) of the noise signal from the noise section detection unit 120 from the average spectrum VR (k) of the observation signal by the configuration of the signal extraction device 600-6, a more accurate target signal Estimate the average spectrum VM (k) of the target signal whitening of the target signal. As a result, the accuracy of the inverse filter coefficient is improved, and the performance of noise suppression and dereverberation of the target signal is improved. Therefore, even if the power of the noise signal Nb (p) is large, noise suppression and reverberation suppression of the target signal are possible. [0043] FIG. 10 shows a functional configuration example of the signal extraction device 600-7 of the third embodiment. The noise section detection unit 120 in the signal extraction device 600-7 receives noise section information Tb (b = 1,..., B) and noise signal for each channel used to detect the noise signal Nb (p). It also outputs Nb (p). The noise interval information Tb is, for example, the time interval t1 to t2 of the noise signal included in the observation signal. The noise interval information Tb is input to the noise correlation function calculation unit 130, and the noise 04-05-2019 14 signal Nb (p) is input to the whitening filter coefficient calculation unit 112. Then, the processes of the whitening filter coefficient calculation unit 112 and the whitening filter unit 100b are performed. [0044] Here, the noise signal included in the whitened observation signal R′b (p) from the whitening filter unit 100 b is also whitened. Also, the time interval of the noise signal included in the observed signal Rb (p) before being whitened and the time of the noise signal N′b (p) included in the whitened observed signal R′b (p) It is almost equal to the target section. Therefore, by using the noise segment T detected by the noise segment detection unit 120, the whitened noise signal N'b (p) can be detected from the whitened observation signal R'b (p). Although the noise correlation function calculation unit 130 performs this detection process in the third embodiment, the present invention is not limited to this. Therefore, it is not necessary to whitenize the noise signal Nb (p) from the noise zone detection unit 120, and the same effect as the signal extraction device 600-6 of the second embodiment can be obtained. [0045] Further, as a modification of the third embodiment, as in the signal extraction device 600-8 shown in FIG. 11, the whitening filter coefficient calculation unit 112 is replaced with the whitening filter coefficient calculation unit 110, and only the observation signal Rb (p) The whitening filter coefficient may be determined using The whitening filter coefficient calculation unit 110 and the whitening filter unit 100b may be omitted from the signal extraction device 600-8 as in the signal extraction device 600-9 shown in FIG. In this case, the noise correlation function calculation unit 130 may detect the observation signal using the noise section information T from the noise section detection unit 120, and obtain the noise correlation function of the detected observation signal. [0046] The figure which shows the function structural example of the conventional signal extraction apparatus 500. FIG. FIG. 2 is a diagram showing an example of a functional configuration of a signal extraction device 600-1 according to the first embodiment. The figure which shows the flow of the main processes of the signal extraction apparatus 600-1. The figure which showed 04-05-2019 15 the detail of Formula (13). FIG. 2 is a diagram showing an example of a functional configuration of a signal extraction device 600-2 according to the first embodiment. FIG. 2 is a diagram showing an example of a functional configuration of a signal extraction device 600-3 according to the first embodiment. FIG. 2 is a diagram showing an example of a functional configuration of a signal extraction device 600-4 according to the first embodiment. FIG. 2 is a diagram showing an example of a functional configuration of a signal extraction device 600-5 according to the first embodiment. FIG. 7 is a diagram showing an example of a functional configuration of a signal extraction device 600-6 according to a second embodiment. FIG. 16 is a diagram showing an example of a functional configuration of a signal extraction device 600-7 according to a third embodiment. FIG. 16 is a diagram showing an example of a functional configuration of a signal extraction device 600-8 according to a third embodiment. FIG. 16 is a diagram showing an example of a functional configuration of a signal extraction device 600-9 according to a third embodiment. 04-05-2019 16

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