Frequency domain variants of velvet noise and their application to speech processing and synthesis: with appendices

Abstract: We propose a new excitation source signal for VOCODERs and an all-pass impulse response for post-processing of synthetic sounds and pre-processing of natural sounds for data-augmentation. The proposed signals are variants of velvet noise, which is a sparse discrete signal consisting of a few non-zero (1 or -1) elements and sounds smoother than Gaussian white noise. One of the proposed variants, FVN (Frequency domain Velvet Noise) applies the procedure to generate a velvet noise on the cyclic frequency domain of DFT (Discrete Fourier Transform). Then, by smoothing the generated signal to design the phase of an all-pass filter followed by inverse Fourier transform yields the proposed FVN. Temporally variable frequency weighted mixing of FVN generated by frozen and shuffled random number provides a unified excitation signal which can span from random noise to a repetitive pulse train. The other variant, which is an all-pass impulse response, significantly reduces "buzzy" impression of VOCODER output by filtering. Finally, we will discuss applications of the proposed signal for watermarking and psychoacoustic research.