Method for separating source signals and apparatus thereof

1. A method for separating a sound source from a mixed signal, comprising:

transforming a mixed signal to channel signals in frequency domain;

grouping several frequency bands for each channel signal to form frequency clusters;

separating the frequency clusters by applying a blind source separation to signals in frequency domain for each frequency cluster; and

integrating the spectrums of the separated signal to restore the sound source in a time domain wherein each of the separated signals expresses one sound source,

wherein said separating the frequency cluster includes:

determining whether or not a channel scrambling problem or a scaling problem is generated in the frequency domain of each cluster;

eliminating the channel scrambling problem, when the channel scrambling problem is generated, by comparing frequency characters of an overlap region in each cluster in said separating the frequency cluster, regarding two clusters having comparatively high likelihood of the overlap region as one sound source, and integrating the two clusters;

eliminating the generated scaling problem, when the scaling problem is generated, by arranging an overlap region between two clusters in said separating the frequency cluster and controlling scaling of the two cluster to have same energy of the overlap region.

2. The method of claim 1 , wherein the likelihood of the overlap region is determined by measuring an Euclidean distance after standardizing output of the each cluster, and the likelihood of the overlap region is determined as high when the measured Euclidean distance is short.

3. The method of claim 1 , wherein the blind source separation technology uses an independent vector analysis (IVA) technology which is a function receiving a vector as input.

4. The method of claim 3 , wherein the IVA technology learns a separation filter to express a separated signal as an independent probability distribution function when a vector is independent from each sound source for overall frequency components of a sound source signal.

5. The method of claim 4 , wherein the probability distribution function is set differently to each cluster to reflect character of the each cluster.

6. The method of claim 4 , wherein statistic characteristics of the probability distribution function is calculated by an equation:

f

si

⁡

(

s

i

)

=

exp

(

-

1

σ

⁢

∑

f

=

1

F

⁢



s

i

f



2

)

,

where s i indicates a i th channel signal, f indicates frequency, S i f indicates component of frequency f in a i th channel signal, and σ denotes signal dispersion.

7. The method of claim 4 , wherein when blind source separation technology is independently applied to a signal corresponding to each cluster, the probability distribution function is calculated by an equation:

f

si

,

c

⁡

(

s

i

,

c

)

=

exp

(

-

1

σ

c

⁢

∑

f

=

F

min

,

c

F

max

,

c

⁢



s

i

,

c

f



2

)

,

where c denotes a cluster index, F min,c indicates a minimum frequency index included in a cluster c, F max,c indicates the maximum frequency index, and σ c indicates the dispersion of a cluster c, and where σ c is differently set to each cluster according to the characteristics of the sound source.

8. An apparatus for separating a sound source from a mixed signal, comprising:

a Fourier transformer for transforming the mixed signal to channel signals in a domain;

a frequency band divider for grouping several frequency bands for each channel signal to form frequency clusters;

a signal separator for separating the frequency clusters by using a blind source separation to signals in frequency domain for each frequency cluster; and

an inverse Fourier transformer for integrating the spectrums of the separated signals to restore the sound source, wherein each of the separated signals expresses one sound source,

wherein the signal separator compares frequency characteristics of an overlap region of each cluster in a cluster division process, regards two clusters having relatively high likelihood of the overlap region as one sound source, and integrates the two clusters to thereby eliminate a channel scrambling generated in the frequency domain for each frequency cluster.

9. The apparatus of claim 8 , wherein the likelihood of the overlap region is determined by measuring an Euclidean distance after standardizing output of the each cluster, and the likelihood of the overlap region is determined as high when the measured Euclidean distance is short.

10. The apparatus of claim 8 , wherein the blind source separation uses an independent vector analysis (IVA) technology which is a function receiving a vector as input.

11. The apparatus of claim 10 , wherein the IVA technology learns a separation filter to express a separated signal as an independent probability distribution function when a vector is independent from each sound source for overall frequency components of a sound source signal.

12. The apparatus of claim 11 , wherein the probability distribution function is set differently to each frequency cluster to reflect character of the each cluster.

13. The apparatus of claim 8 , wherein a frequency cluster for the each channel signal is formed by applying clustering of Mel scale.