SIGNAL RECONSTRUCTION FROM STFT MAGNITUDE : A STATE OF THE ART

By Nicolas Sturmel, Laurent Daudet. 2011

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Jukebox: A Generative Model for Music (2020, methodology): “As we use nonautoregressive decoders with continuous representation for output, we report spectral convergence (Sturmel & Daudet, 2011), which measures the amount of spectral error relative to signal, as test error and proxy for reconstruction ﬁdelity.”
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction (2018, background): “It is well-known that this incurs a phase inconsistency problem [11, 12, 13], especially for speech processing, where there is typically at least half overlap between consecutive frames.”
Phase Reconstruction from Amplitude Spectrograms Based on Von-Mises-Distribution Deep Neural Network (2018, background): “5 shows the log spectral convergence [25] of “PH (4 kHz)” and “PH+GD (4 kHz).”
A Comparison of Recent Neural Vocoders for Speech Signal Reconstruction (2019, background): “[22] N. Sturmel and L. Daudet, “Signal Reconstruction from STFT magnitude : A State of the Art,” in Proceedings of the International Conference on Digital Audio Effects (DAFx), Paris, France, September 2011, pp. 375–386.”