Empirical Wavelets

Adaptive (i.e., data-driven) methods have become very popular these last decades. Among the existing techniques, the empirical mode decomposition has proven to be very efficient in extracting accurate time-frequency information from non-stationary signals. However, it is a purely algorithmic method and lacks of solid theoretical foundations. To overcome this issue, we propose the construction of adaptive wavelets, called empirical wavelets. Their aim is to decompose a signal into its harmonic

Adaptive (i.e., data-driven) methods have become very popular these last decades. Among the existing techniques, the empirical mode decomposition has proven to be very efficient in extracting accurate time-frequency information from non-stationary signals. However, it is a purely algorithmic method and lacks of solid theoretical foundations. To overcome this issue, we propose the construction of adaptive wavelets, called empirical wavelets. Their aim is to decompose a signal into its harmonic

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

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