AES Europe 2026

High-fidelity vocal processing is frequently compromised by
sibilance, a phenomenon characterized by stochastic
high-frequency energy that presents unique dynamic range
challenges. While traditional de-essing techniques often
rely on static frequency bands, they fail to account for
inter-speaker variability; changing dynamics. This
project presents an adaptive real-time de-essing
application, developed using the JUCE framework, which
automatically detects; suppresses sibilant frequencies.
The proposed methodology integrates a derivative-based
frequency tracking algorithm to estimate the spectral
centroid without the computational overhead of the Fast
Fourier Transform (FFT). This is coupled with a dual-path
envelope detection system; a relative threshold logic to
distinguish sibilance from the wideband signal.
Additionally, a dynamic harmonic exciter is implemented to
restore high-frequency presence during non-sibilant
periods. Objective spectral analysis confirms the system's
ability to selectively attenuate energy in the 6–11 kHz
range while maintaining spectral transparency;
minimizing artifacts.