Blind Signal Separation (BSS) is used in many digital signal processing applications where signal separation using blind methods is applicable, including acoustics, radio communications, as well as image processing. Please contact us to discuss your specific application requirements.
Blind Signal Separation
Blind Signal Separation or Blind Source Separation is the separation of a set of signals from a set of mixed signals without the aid of information (or with very little information) about the signal source or the mixing process.
Blind source separation relies on the assumption that the source signals do not correlate with each other. For example, a set of signals may be statistically independent or decorrelated. Because of this independence, the set can be separated into another signal set, such that the regularity of each resulting signal is maximized, and the regularity between the different signals is minimized (i.e. statistical independence is maximized).
Applications of BSS
Signal separation using these blind techniques has found many applications in acoustics, where different sound sources are recorded simultaneously either with individual microphones or microphone arrays. These sources may be speech or music, or an underwater signal recorded with passive sonar. In these cases it can be especially useful for noise reduction processing where the signals of interest are isolated from interferes and other noise sources.
Other applications for blind source separation include radio communications, where it is used to differentiate the mixtures of communication signals received by antenna arrays. The method has also been applied to image processing as well as used in the processing of biomedical markers like electrocardiogram (EKG/ECG), electromyogram (EMG) and other bio-potentials.
Typical methods for blind source separation include:
- Principal components analysis (PCA)
- Singular value decomposition (SVD)
- Independent component analysis (ICA)
- Dependent component analysis (DCA)
- Short-time Fourier transform (STFT)
- Degenerate unmixing estimation technique (DUET)
- W-disjoint orthogonality
- Joint approximate diagonalization eigen-matrices (JADE)
- Computational auditory scene analysis (CASA)
- Constant modulus algorithm (CMA)