Audio Volume Leveling With Category-Based Gain Curves
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Solution Overview
Problem
Conventional automatic volume control and leveling systems apply the same processing to dissimilar audio sources, leading to suboptimal sound quality and user experience, as they fail to distinguish between different audio categories like music and voice, resulting in distortion and inconsistent volume levels.
Innovation Solution
The system employs different gain curves based on audio categories, sources, and desired volume levels, using adaptive noise gates and signal-to-noise ratio adjustments to optimize output audio quality by applying distinct processing for music, voice, and other audio types, ensuring consistent volume and reduced distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the same automatic volume control processing is applied to all audio sources, then the system complexity is reduced and ease of operation is improved, but the sound quality deteriorates and distortion increases due to failure to distinguish between different audio categories
Solution Approach 1:
The system applies different gain curves and processing parameters to different audio categories (music, voice, other) by detecting audio characteristics and selectively applying appropriate processing. This allows optimized sound quality for each audio type while maintaining automated operation through audio-based classification.
2Manufacturing precision
If different gain curves are applied based on audio categories, then the sound quality and volume consistency are improved, but the device complexity increases due to multiple processing paths
Solution Approach 1:
The system changes processing parameters (gain curves, compression ratios, attack/release times) based on detected audio category. The audio detection component identifies whether the input is music, voice, or other audio, and the system automatically adjusts processing parameters accordingly, achieving high sound quality without requiring manual configuration of multiple processing paths.
3Stability of the object's composition
If conventional volume leveling is applied to music, then volume consistency is improved, but dynamic range compression causes loss of audio quality and detail
Solution Approach 1:
The system applies dynamic processing parameters specifically for music audio, using slower attack and release times that preserve the natural dynamics of musical performances. The gain curve application is adapted to maintain volume consistency while preserving dynamic range, allowing both soft and loud passages to retain their intended character.
4Object-affected harmful factors
If aggressive noise reduction is applied to improve clarity, then the noise level is reduced, but the natural sound characteristics and dynamic range are lost
Solution Approach 1:
The system applies noise reduction processing selectively based on audio category detection. For voice audio, moderate noise reduction is applied to improve clarity while preserving natural characteristics. For music audio, minimal noise reduction is applied to maintain dynamic range and natural sound quality, demonstrating locally optimized processing for different audio types.
Data Source
AI summary
A system that performs automatic volume control and leveling (AVCL) based on audio category and desired volume level. The system may select different settings for audio data associated with different audio sources (e.g., content providers), audio categories (e.g., types of audio data, such as music, voice, etc.), genres, and/or the like. For example, the system may distinguish between music signals and voice signals (e.g., speech) and may apply a first gain curve for the music and a second gain curve for the speech. The gain curve may include an adaptive noise gate to reduce in-channel noise, such as by attenuating quiet sounds. The system may select the gain curve based on a desired volume level, and in some examples may modify the gain curve based on a signal-to-noise ratio (SNR) of the input audio and/or based on environmental noise.