Process / pipelineAdaptive wavelet decomposition
Empirical Wavelet Transform
The empirical wavelet transform (EWT) is a data-driven wavelet decomposition method that automatically defines wavelet bases adapted to the frequency content of the signal. Introduced by Jérémie Gilles (2013), it overcomes a key limitation of classical wavelets—which use fixed, predefined bases—by constructing custom wavelets from the signal's own spectrum. This adaptive approach is particularly effective for analyzing non-stationary signals with complex, multi-component structures.
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Sources
- Gilles, J. (2013). Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16), 3999–4010. DOI: 10.1109/TSP.2013.2265902 ↗
- Gilles, J. (2015). Empirical wavelet transform for multiscale analysis of signals. IEEE Signal Processing Magazine, 32(6), 125–130. DOI: 10.1109/MSP.2015.2432072 ↗
- Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544. DOI: 10.1109/TSP.2013.2288675 ↗