Thanks for this great job! I have a question about the input feature of DNN model.
In the original PercepNet paper, pitch period T is used as an feature to the DNN input. In this implementation project code script denoise.cpp, it's calculated as (float)noisy->last_period/(PITCH_MAX_PERIOD-3*PITCH_MIN_PERIOD), and in the RNNoise implementation, it's calculated as .01*(pitch_index-300) , so what's the difference between these two implementations and could you please explain the reason behind this? Thanks a lot.
Thanks for this great job! I have a question about the input feature of DNN model.
In the original PercepNet paper, pitch period T is used as an feature to the DNN input. In this implementation project code script denoise.cpp, it's calculated as
(float)noisy->last_period/(PITCH_MAX_PERIOD-3*PITCH_MIN_PERIOD),and in the RNNoise implementation, it's calculated as.01*(pitch_index-300), so what's the difference between these two implementations and could you please explain the reason behind this? Thanks a lot.