Automatic De-Esser Using Relative Sibilance Detection
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Solution Overview
Problem
Conventional de-essers require manual parameter settings and are ineffective in reducing sibilance in both loud and soft parts of a performance, as they rely on absolute signal levels, leading to inconsistent results and over-processing or under-processing of sibilant sounds.
Innovation Solution
An automatic de-esser that processes audio signals by dividing them into buffers, transforming them into the frequency domain, and applying multi-band compression based on relative energy comparisons and zero-crossing rates to determine attenuation, independent of absolute signal levels, allowing for consistent sibilance reduction across varying audio levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional de-essers use absolute threshold parameters for gain reduction, then they can reduce sibilance in loud parts of performance, but they fail to effectively reduce sibilance in soft parts and require manual intervention
Solution Approach 1:
The de-esser automatically detects sibilance and adjusts compression parameters without manual intervention by analyzing the input signal characteristics. The system self-adjusts the threshold and ratio based on detected sibilance events, eliminating the need for sound engineers to manually tweak parameters for different performance sections.
Solution Approach 2:
The de-esser dynamically adapts its compression parameters (threshold, ratio, attack, release) based on the instantaneous characteristics of the input signal. This allows the device to respond differently to loud versus soft sibilance events, maintaining effectiveness across varying performance intensities without manual reconfiguration.
2Stability of the object's composition
If conventional de-essers rely on absolute signal levels, then they provide consistent processing at fixed levels, but they produce inconsistent results when overall signal level changes
Solution Approach 1:
The de-esser changes its operating parameters dynamically based on the input signal level. Instead of using fixed absolute thresholds, the system adjusts compression parameters proportionally to the detected signal characteristics, ensuring consistent sibilance reduction whether the overall performance is loud or soft.
Solution Approach 2:
The system transitions from static absolute threshold compression to dynamic relative compression. The de-esser continuously monitors signal level and adapts its threshold and ratio parameters in real-time, maintaining processing consistency across varying overall signal levels without requiring manual intervention.
3Object-affected harmful factors
If de-essing is applied as multi-band compression on sibilance frequency range, then sibilance can be reduced without affecting other frequency ranges, but precise detection of sibilance energy requires complex analysis
Solution Approach 1:
The de-esser segments the audio spectrum by applying the compression effect only to the sibilance frequency range (typically 4-10 kHz) while leaving other frequency ranges unaffected. This frequency-selective approach reduces sibilance without altering the overall tonal balance of the vocal performance.
Solution Approach 2:
The system extracts and analyzes only the relevant sibilance frequency components for detection and compression decisions. By focusing computational analysis on the specific frequency range where sibilance occurs, the system achieves precise sibilance detection without requiring complex full-spectrum analysis.
Data Source
AI summary
Methods, systems, and computer program products of automatic de-essing are disclosed. An automatic de-esser can be used without manually setting parameters and can perform reliable sibilance detection and reduction regardless of absolute signal level, singer gender and other extraneous factors. An audio processing device divides input audio signals into buffers each containing a number of samples, the buffers overlapping one another. The audio processing device transforms each buffer from the time domain into the frequency domain and implements de-essing as a multi-band compressor that only acts on a designated sibilance band. The audio processing device determines an amount of attenuation in the sibilance band based on comparison of energy level in sibilance band of a buffer to broadband energy level in a previous buffer. The amount of attenuation is also determined based on a zero-crossing rate, as well as a slope and onset of a compression curve.