As known in the art, blind source separation is a technology for separating a signal collected from more than two microphones depending on the statistic characteristics of sound sources. The blind source separation is generally classified into a time domain based separation method and a frequency domain based separation method.
In general, the blind source separation performs learning by using an independent component analysis (ICA) method. The ICA method is an algorithm for separating a voice signal only from an input signal in which the voice signal and noise signals are mixed together through a microphone array system on the assumption that each signal source has independent characteristics.
The ICA method is employed to find an inverse matrix of a mixing matrix to find a separation matrix for separating a voice signal from an input signal. In this case, the inverse matrix can be calculated only if the number of sound sources is identical with the number of the mixing matrixes.
As described above, in order to eliminate noise by using the blind source separation, original signals are separated from input signals having voice signals and noise signals by extracting the voice and noise signals that are mutually independent from the input signal. In other words, a mixed signal having a plurality of voice signals and noise signals is received, the voice signals and the noise signals are separated from the mixed signal, and voice recognition is performed only by using the separated voice signals.
However, the time domain-based separation method has following disadvantages although the time domain-based separation method has better performance than the frequency domain-based separation method. That is, the time domain based separation method is significantly influenced by a location of speakers and environmental factors. Also, the algorithm of the time domain based separation method becomes complicated and the computation amount thereof becomes increased in case of separating more than three signals. Meanwhile, the frequency domain-based separation method also has shortcoming such as a serious scrambling problem although the algorithm thereof is very simple to implement and intuitive. It is, therefore, difficult to solve such a scrambling problem of the frequency domain-based separation method.
In order to overcome the scrambling problem, an independent vector analysis method has been introduced. The independent vector analysis (IVA) method separates sound sources by regarding overall frequency bands as one vector. However, the independent vector analysis method has disadvantages of large computation amount and slow convergence.
The ICA method has a limitation that the number of mixed signals input to an input device should be identical with the number of original signal sources and that the number of separated signals is identical with the number of signal sources. Further, it is difficult to detect which of separated signals is related to which of signal sources.
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