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phasenn
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updated README

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David 2020-11-07 17:01:52 +10:30
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@ -33,7 +33,7 @@ Quality similar to DSP based techniques (e.g. Hilbert Transforms, sampling LPC f
## Example
Here is the output from [phasenn_test11.py](phasenn_test11.py). The first plot is a series of (log) magnitude spectra of simulated speech frames. The voiced frames have two fairly sharp peaks (formants) beneath Fs/2 with structured phase consisting of linear and dispersive terms. Unvoiced frames have less sharp peaks above Fs/2, and random phases.
Here is the output from [phasenn_test11.py](phasenn_test11.py). The first plot is a series of (log) magnitude spectra of **simulated** speech frames. The voiced frames have two fairly sharp peaks (formants) beneath Fs/2 with structured phase consisting of linear and dispersive terms. Unvoiced frames have less sharp peaks above Fs/2, and random phases.
![](example_mag.png "Magnitude Spectra")
![](example_phase.png "Phase Spectra")
@ -47,5 +47,7 @@ For unvoiced speech, we want the NN output (blue) to be random. They do not nee
## References
[1] Wang et al, "Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis", 2019
[2] Engel et all, "DDSP: DIFFERENTIABLE DIGITAL SIGNAL PROCESSING", 2020
[3] Liu et al, "Neural Homomorphic Vocoder", 2020