Open ahmed-fau opened 5 years ago
@ahmed-fau, I assume by this you mean features such as "Perceptual Linear Predictive (PLP) Analysis of Speech"?
@cpuhrsch PLP is an advanced version of LPC which incorporates perceptual modeling for linear prediction. LPC is simpler as you only estimate the analysis filter parameters from the auto-correlation matrix of a speech frame in time domain. This is already provided in Python via AudioLazy. So, it could be interfaced with torchaudio to provide LPC analysis/synthesis features which are being used in current generative models for speech synthesis (e.g., LPCNet).
@ahmed-fau - and presumably also used as a building block for PLP?
Adding @vincentqb
In principle: PLP applies linear prediction within its estimation pipeline. However, I am not sure whether a toolkit like Kaldi provides linear prediction in a direct fashion for that purpose. If so, then I think all that we need would be to get a plain interface through torchaudio to that functionality.
@cpuhrsch if you check the original PLP paper at Fig.1, you can find linear prediction is a dedicated functionality during the estimation process. The last block in the diagram (called: solution for auto-regressive coefficients) is exactly the functionality I am speaking about. When you apply this processing block directly to raw speech waveform (i.e., without all the preceding blocks), these coefficients correspond to LPC.
Hi, Is there any plan to provide torchaudio with a new feature of calculating the LPC analysis parameters for speech signals? Or at least converting MFCC to LPC?