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Source Separation Using Temporal Predictability

Human listeners have the, extraordinary ability to hear and recognize speech even when more than one person is talking. Their machine counterparts have historically been unable to compete with this ability, until now.

The two speaker single microphone source separation problem is one of the most challenging source separation scenarios and only a few quantitative results have been reported in the literature.

A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals.


  • Krishman Tamrakar
  • Arjan Deep Dhakal
 

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