Automatic Volume Leveling With Audio-Specific Gain Curves
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Solution Overview
Problem
Conventional automatic volume leveling techniques apply the same process to dissimilar audio sources, such as music and voice, leading to suboptimal sound quality and potential distortion, as they fail to differentiate between different audio categories and genres, resulting in inconsistent user experiences.
Innovation Solution
The system performs automatic volume leveling by using distinct gain curves based on audio categories, genres, and desired volume levels, selectively applying different gain curves for music, voice, and other audio types to optimize sound quality and maintain consistent volume levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the same gain curve is applied to all audio sources, then the device complexity is reduced and ease of operation is improved, but the sound quality deteriorates and distortion increases
Solution Approach 1:
The system applies different gain curves to different audio categories (music, voice, podcast, video) based on local characteristics of each audio type. The audio processing device categorizes incoming audio and selects appropriate gain curves from a library, allowing each audio category to receive optimized processing tailored to its specific characteristics rather than a universal approach.
2Manufacturing precision
If different gain curves are applied to different audio categories, then the sound quality is improved and distortion is reduced, but the device complexity increases
Solution Approach 1:
The system segments the audio processing task into distinct categories (music, voice, podcast, video) with dedicated gain curves for each. By dividing the audio space into discrete categories and assigning specific processing parameters to each, the system manages complexity through organization rather than attempting continuous optimization across all audio types simultaneously.
Solution Approach 2:
The system changes processing parameters (gain curves) based on the detected audio category. A library of pre-defined gain curves with different characteristics is maintained, and the system selects and applies the appropriate curve based on audio classification, allowing parameter optimization without requiring complex real-time calculation of custom curves.
3Ease of operation
If conventional volume leveling is applied to all audio, then ease of operation is maintained, but the user experience becomes inconsistent across different audio sources
Solution Approach 1:
The system dynamically adapts the volume leveling process based on the detected audio category. Rather than using a static, one-size-fits-all approach, the system automatically adjusts processing parameters in real-time based on audio characteristics, providing consistent and optimized user experience across diverse audio sources while maintaining automatic operation.
Data Source
AI summary
A system that includes an automatic volume leveler (AVL) that processes audio data 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. Additionally or alternatively, the system may distinguish between different genres of music and may apply different gain curves based on the genre. Further, the system may select a gain curve based on a desired volume level. Therefore, output audio data generated by performing AVL may be optimized based on the audio category, audio source, genre, desired volume level, and/or the like.