Signal separation method, signal separation device and recording medium
First Claim
1. A signal separation method that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a procedure that transforms the observed signal values observed by said sensors into frequency-domain signal values,a procedure that uses said frequency-domain signal values to calculate at each frequency the relative values of the observed values between said sensors (including mapping these relative values),a procedure that clusters said relative values into N clusters,a procedure that calculates a representative value for each of said clusters,a procedure that uses said representative values to generate a mask for the purpose of extracting, from said frequency-domain signal values, mixed signal values comprising the signals emitted from V (2≦
V≦
M) signal sources,a procedure that uses said mask to extract said mixed signal values from said frequency-domain signal values, anda procedure that separates and extracts the values of V signals from said mixed signal values.
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Abstract
A method and a device for signal separation. First, values of signals observed by M sensors are transformed into frequency domain values, and these frequency domain values are used to calculate relative values of the observed values between the sensors at each frequency. These relative values are clustered into N clusters, and the representative value of each cluster is calculated. Then, using these representative values, a mask is produced to extract the values of the signals emitted by V (1≦V≦M) signal sources from the frequency-domain signal values, and this mask is applied to the frequency-domain signal values. After that, if V=1 then the limited signal is output directly as a separated signal, while if V≧2 then the separated values are obtained by separating this limited signal with separation techniques such as ICA.
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Citations
30 Claims
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1. A signal separation method that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a procedure that transforms the observed signal values observed by said sensors into frequency-domain signal values, a procedure that uses said frequency-domain signal values to calculate at each frequency the relative values of the observed values between said sensors (including mapping these relative values), a procedure that clusters said relative values into N clusters, a procedure that calculates a representative value for each of said clusters, a procedure that uses said representative values to generate a mask for the purpose of extracting, from said frequency-domain signal values, mixed signal values comprising the signals emitted from V (2≦
V≦
M) signal sources,a procedure that uses said mask to extract said mixed signal values from said frequency-domain signal values, and a procedure that separates and extracts the values of V signals from said mixed signal values. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
- 2) signals are mixed together and observed with M sensors, comprising;
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10. A signal separation method that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a procedure that transforms the observed signal values observed by said sensors into frequency-domain signal values, a procedure that uses said frequency-domain signal values to calculate at each frequency the relative values of the observed values between said sensors (including mapping these relative values), a procedure that clusters said relative values into N clusters, a procedure that calculates a representative value for each of said clusters, a procedure that generates a mask function that takes a high level value for said relative values that are within a prescribed range that includes one of the said representative values, and takes a low level value for said representative values that are not inside said prescribed range, wherein the transitions from said high level value to said low level value that accompany changes of said relative value occur in a continuous fashion, and a procedure that multiplies said frequency-domain signal values by said mask to extract the signal emitted from one signal source.
- 2) signals are mixed together and observed with M sensors, comprising;
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11. A signal separation method that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a procedure that transforms the observed signal values x1(t), . . . , xM(t) observed by said sensors into frequency-domain signal values X1(f,m), . . . , XM(f,m), a procedure that clusters first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)] comprising said frequency-domain signal values X1(f,m), . . . , XM(f,m) into N clusters Ci(f) (i1, . . . , N) at each frequency f, a procedure that calculates second vectors ai(f) to represent each said cluster Ci(f), a procedure that extracts V (1≦
V≦
M) third vectors ap(f) (p=1, . . . , V) from said second vectors ai(f),a procedure that generates a mask M(f,m) represented by the formula where Gk is the set of said third vectors ap(f), Gkc is the complementary set of Gk, and D(α
,β
) is the Mahanalobis square distance between the vectors α and
β
,and a procedure that extracts the signal values emitted from V of said signal sources by calculating the product of said mask M(f,m) and said first vectors X(f,m). - View Dependent Claims (13, 14)
- 2) signals are mixed together and observed with M sensors, comprising;
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14. A signal separation method according to claim 13, wherein said clustering procedure is performed after performing the calculation
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( f , m ) ← { X ( f , m ) / X ( f , m ) ( X ( f , m ) ≠ 0 ) X ( f , m ) ( X ( f , m ) = 0 ) (where the notation ∥
X(f,m)∥
denotes the norm of X(f,m)).After said formula
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12. A signal separation method that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, wherein
a procedure that transforms the observed signal values x1(t), . . . , xM(t) observed by said sensors into frequency-domain signal values X1(f,m), . . . , XM(f,m), a procedure that clusters first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)] comprising said frequency-domain signal values X1(f,m), . . . , XM(f,m) into N clusters Ci(f) (i=1, . . . , N) at each frequency f, a procedure that calculates second vectors ai(f) to represent each said cluster C1(f), a procedure that extracts V (1≦
V≦
M) third vectors ap(f) (p=1, . . . , V) from said second vectors ai(f),and a procedure that judges whether or not said first vectors X(f,m) satisfy the relationship
maxap (f)∈
Gk D(X(f,m)ap(f))<
minaq (f)∈
Gk c D(X(f,m),aq(f))
Formula 56where Gk is the set of said third vectors ap(f), Gkc is the complementary set of Gk, and D(α
, β
) is the Mahanalobis square distance between the vectors α
and β
, and, if so, extracts said first vectors X(f,m) as the signal values emitted from V of the said signal sources.
- 2) signals are mixed together and observed with M sensors, wherein
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15. A signal separation method that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising
a procedure that transforms the observed signal values x1(t), . . . , xM(t) observed by said sensors into frequency-domain signal values X1(f,m), . . . , XM(F,m), a procedure that clusters first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)]T comprising said frequency-domain signal values X1(f,m), . . . , XM(gm) into N clusters Ci(f) (i=1, . . . , N) at each frequency f, a procedure that calculates second vectors ai(f) to represent each said cluster Ci(f), a procedure that calculates an N-row×
M-column separation matrix W(f,m) that is the Moore-Penrose pseudo-inverse matrix of an M-row×
N-column matrix in which 0 or more of the N said second vectors ai(f) are substituted with zero vectors,and a procedure that calculates a separated signal vector Y(f,m)=[Y1(f,m), . . . , YN(f,m)]T by performing the calculation Y(f,m)=W(f,m)X(f,m). - View Dependent Claims (16, 17, 18, 19, 20)
- 2) signals are mixed together and observed with M sensors, comprising
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21. A signal separation device that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a memory unit that stores the observed signal values observed by said sensors; and a processor which is connected to said memory unit and is configured to transform said observed signal values into frequency-domain signal values, to use said frequency-domain signal values to calculate at each frequency the relative values of the observed values between said sensors (including mapping these relative values), to cluster said relative values into N clusters, to calculate a representative value for each of said clusters, to use said representative values to generate a mask for the purpose of extracting, from said frequency-domain signal values, mixed signal values comprising the signals emitted from V (2≦
V≦
M) signal sources, to use said mask to extract said mixed signal values from said frequency-domain signal values, and to separate and extract the values of V signals from said mixed signal values.
- 2) signals are mixed together and observed with M sensors, comprising;
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22. A signal separation device that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a memory unit that stores the observed signal values observed by said sensors; and a processor which is connected to said memory unit and is configured to transform said observed signal values into frequency-domain signal values, to use said frequency-domain signal values to calculate at each frequency the relative values of the observed values between said sensors (including mapping these relative values), to cluster said relative values into N clusters to calculate a representative value for each of said clusters, to generate a mask, which is a function that takes a high level value for said relative values that are within a prescribed range that includes one said representative value, and takes a low level value for said representative values that are not inside said prescribed range, and where the transitions from said high level value to said low level value that accompany changes of said relative value occur in a continuous fashion, and to extract the values of a signal emitted from one signal source by multiplying said frequency-domain values by said mask.
- 2) signals are mixed together and observed with M sensors, comprising;
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23. A signal separation device that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a memory unit that stores the observed signal values x1(t), . . . , xM(t) observed by said sensors; and a processor which is connected to said memory unit and is configured to transform said observed signal values x1(t), . . . , xM(t) into frequency-domain signal values X1(f,m), . . . , XM(f,m), to cluster first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)] comprising said frequency-domain signal values X1(f,m), . . . , XM(f,m) into N clusters Ci(f) (i=1, . . . , N) at each frequency f, to calculate second vectors ai(f) to represent each said cluster Ci(f), and extracts V (1≦
V≦
M) third vectors ap(f) (p=1, . . . , V) from said second vectors ai(f), to generate a mask M(f,m) represented by the formulawhere Gk is the set of said third vectors ap(f), Gkc is the complementary set of Gk, and D(α
β
) is the Mahanalobis square distance between the vectors α and
β
, and to extract the signal values emitted from V of the said signal sources by calculating the product of said mask M(f,m) and said first vectors X(f,m).
- 2) signals are mixed together and observed with M sensors, comprising;
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24. A signal separation device that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a memory unit that stores the observed signal values x1(t), . . . , xM(t) observed by said sensors; and a processor which is connected to said memory unit and is configured to transform said observed signal values x1(t), . . . , xM(t) into frequency-domain signal values X1(f,m), . . . , XM(f,m), to cluster first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)] comprising said frequency-domain signal values X1(f,m), . . . , XM(f,m) into N clusters Ci(f) (i=1, . . . , N) at each frequency f, to calculate second vectors ai(f) to represent each said cluster Ci(f), to extract V (1≧
V≧
M) third vectors ap(f) (p=1, . . . , V) from said second vectors ai(f), to judge whether or not said first vectors satisfy the relationship
maxap (f)∈
Gk D(X(f,m),ap(f))<
minaq (f)∈
Gk g D(X(f,m),aq(f))
Formula 62where Gk is the set of said third vectors ap(f), Gkc is the complementary set of Gk, and D(α
,β
) is the Mahanalobis square distance between the vectors α
z and β
, and to extract said first vectors X(f,m) satisfying said relationship as the signal values emitted from V of the said signal sources.
- 2) signals are mixed together and observed with M sensors, comprising;
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25. A signal separation device that separates and extracts signals under conditions where N (N≧
- 2) signals are mixed together and observed with M sensors, comprising;
a memory unit that stores the observed signal values x1(t), . . . , xM(t) observed by said sensors; and a processor which is connected to said memory unit and is configured to transform said observed signal values X1(t), . . . , XM(t) into frequency-domain signal values X1(f,m), . . . , XM(f,m), to cluster first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)]T comprising said frequency-domain signal values X1(f,m), . . , XM(gm) into N clusters Ci(f) (i=1, . . , N) at each frequency f, to calculate second vectors ai(f) to represent each said cluster Ci(f), to calculate an N-row×
M-column separation matrix W(f,m) that is the Moore-Penrose pseudo-inverse matrix of an M-row×
N-column matrix in which 0 or more of the N said second vectors ai(f) are substituted with zero vectors, and to calculate a separated signal vector Y(f,m)=[Y1(f,m), . . . , YN(f,m)]T by performing the calculation Y(f,m)=W(f,m)X(f,m).
- 2) signals are mixed together and observed with M sensors, comprising;
-
26. A computer readable medium storing a signal separation program, which when executed by a computer causes the computer to perform:
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a procedure that transforms observed signal values, which are mixtures of N (N≧
2) signals observed with M sensors, into frequency-domain values,a procedure that uses said frequency-domain signal values to calculate at each frequency the relative values of the observed values between said sensors (including mapping these relative values), a procedure that clusters said relative values into N clusters, a procedure that calculates a representative value for each of said clusters, a procedure that uses said representative values to generate a mask for the purpose of extracting, from said frequency-domain signal values, mixed signal values comprising the signals emitted from V (2≦
V≦
M) signal sources,a procedure that uses said mask to extract said mixed signal values from said frequency-domain signal values, and a procedure that separates and extracts the values of V signals from said mixed signal values.
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27. A computer readable medium storing a signal separation program, which when executed by a computer, causes the computer to perform:
-
a procedure that transforms observed signal values, which are mixtures of N (N≧
2) signals observed with M sensors, into frequency-domain values,a procedure that uses said frequency-domain signal values to calculate at each frequency the relative values of the observed values between said sensors (including mapping these relative values), a procedure that clusters said relative values into N clusters, a procedure that calculates a representative value for each of said clusters, a procedure that generates a mask, which is a function that takes a high level value for said relative values that are within a prescribed range that includes one of said representative values, and takes a low level value for said representative values that are not inside said prescribed range, wherein the transitions from said high level value to said low level value that accompany changes of said relative value occur in a continuous fashion, and a procedure that extracts the signal values emitted from one signal source by multiplying said frequency-domain signal values by said mask.
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28. A computer readable medium storing a signal separation program, which when executed by a computer, causes the computer to perform:
-
a procedure that transforms observed signal values xi(t), . . . , xM(t), which are mixtures of N (N≧
2) signals observed by M sensors, into frequency-domain signal values X1(f,m), . . . , XM(f,m).a procedure that clusters first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)] comprising said frequency-domain signal values X1(f,m), . . . , XM(f,m) into N clusters Ci(f) (i=1, . . . , N) at each frequency f, a procedure that calculates second vectors ai(f) to represent each said cluster Ci(f), a procedure that extracts V (1≦
V≦
M) third vectors ap(f) (p=1, . . . , V) from said second vectors ai(f),a procedure that generates a mask M(f,m) represented by the formula where Gk is the set of said third vectors ap(f), Gkc is the complementary set of Gk, and D(α
,β
) is the Mahanalobis square distance between the vectors α and
β
,and a procedure that extracts the signal values emitted from V of said signal sources by calculating the product of said mask M(f,m) and said first vectors X(f,m).
-
-
29. A computer readable medium storing signal separation program, which when executed by a computer, causes the computer to perform:
-
a procedure that transforms observed signal values x1(t), . . . , xM(t), which are mixtures of N (N≧
2) signals observed by M sensors, into frequency-domain signal values X1(f,m), . . , XM(f,m),a procedure that clusters first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)] comprising said frequency-domain signal values X1(f,m), . . . , XM(f,m) into N clusters Ci(f) (i=1, . . . , N) at each frequency f, a procedure that calculates second vectors ai(f) to represent each said cluster Ci(f), a procedure that extracts V (1≦
V≦
M) third vectors ap(f) (p=1, . . . , V) from said second vectors ai(f), and a procedure that judges whether or not said first vectors X(f,m) satisfy the relationship
maxap (f)∈
Gk D(X(f,m),ap(f))<
minaq (f)∈
Gk c D(X(f,m),aq(f))
Formula 64where Gk is the set of said third vectors ap(f), Gkc is the complementary set of Gk, and D(α
,β
) is the Mahanalobis square distance between the vectors α and
β
, and, if so, extracts said first vectors X(f,m) as the signal values emitted from V of the said signal sources.
-
-
30. A computer readable medium storing a signal separation program, which when executed by a computer, causes the computer to perform:
-
a procedure that transforms observed signal values x1(t), . . . , xM(t), which are mixtures of N (N≧
2) signals observed by M sensors, into frequency-domain signal values X1(f,m), . . . , XM(f,m),a procedure that clusters first vectors X(f,m)=[X1(f,m), . . . , XM(f,m)]T comprising said frequency-domain signal values X1(f,m), . . . , XM(f,m) into N clusters Ci(f) (i=1, . . . , N) at each frequency f, a procedure that calculates second vectors ai(f) to represent each said cluster Ci(f), a procedure that calculates an N-row×
M-column separation matrix W(f,m) that is the Moore-Penrose pseudo-inverse matrix of an M-row×
N-column matrix in which 0 or more of the N said second vectors ai(f) are substituted with zero vectors,and a procedure that calculates a separated signal vector Y(f,m)=[Y1(f,m), . . , YN(f,m)]T by performing the calculation Y(f,m)=W(f,m)X(f,m).
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Specification