Frequency domain variants of velvet noise and their application to speech processing and synthesis: with appendices
This work addresses speech processing and synthesis challenges for vocoder-based systems, offering incremental improvements in signal quality and versatility.
The authors tackled the problem of generating smoother excitation signals for vocoders and reducing buzzy artifacts in synthetic speech by proposing frequency-domain variants of velvet noise, achieving a unified excitation signal that spans from random noise to repetitive pulse trains and significantly reducing buzzy impressions through filtering.
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.