Audio Volume Normalization Using Metadata and Buffering
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Solution Overview
Problem
Conventional audio media systems face challenges in maintaining consistent volume levels across different sources and media, leading to irritating fluctuations and potential hearing damage, as dynamic range compression alters the original dynamics of audio signals.
Innovation Solution
The use of media metadata to determine average audio levels for volume adjustment, combined with dynamic range compression and audio watermarking or fingerprinting to identify media, ensures consistent volume levels through gain adjustments and real-time monitoring, while buffering prevents rapid volume changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If dynamic range compression is used to adjust volume levels, then volume consistency is improved, but the original dynamics of audio signals are altered
Solution Approach 1:
The system performs preliminary identification of media using audio watermarking or fingerprinting before volume adjustment. By identifying the media in advance, the system can retrieve pre-stored volume metadata and apply appropriate gain values without needing to dynamically compress the audio signal, thus preserving original dynamics while achieving volume consistency.
Solution Approach 2:
The system introduces an intermediary metadata layer that stores volume information for identified media. This metadata acts as a mediator between the audio signal and volume adjustment mechanism, allowing the system to apply precise gain values based on retrieved metadata rather than using aggressive dynamic range compression, thereby maintaining audio quality while achieving volume normalization.
2Stability of the object's composition
If volume levels are continuously monitored and adjusted in real-time, then volume consistency is improved, but computational complexity increases
Solution Approach 1:
The system performs media identification and volume metadata retrieval in advance, before actual playback. By preparing the gain values beforehand based on identified media characteristics, the system avoids the need for complex real-time continuous monitoring and adjustment, reducing computational complexity while maintaining volume consistency through pre-calculated gain application.
Solution Approach 2:
The system uses the audio signal's own embedded watermark or fingerprint to automatically identify the media and retrieve its volume metadata. This self-service approach eliminates the need for external databases or complex manual configuration, allowing the system to autonomously determine appropriate gain values with minimal computational overhead.
3Measurement precision
If audio watermarking or fingerprinting is used to identify media, then volume adjustment accuracy is improved, but processing time increases
Solution Approach 1:
The system performs media identification and metadata retrieval in parallel with audio buffering, rather than sequentially. By rushing through the identification process concurrently with other operations and using efficient watermarking algorithms, the system minimizes the time penalty while maintaining high volume adjustment accuracy through precise media identification.
Data Source
AI summary
Apparatus, systems, articles of manufacture, and methods for volume adjustment are disclosed herein. An example method includes collecting data corresponding to a volume of an audio signal as the audio signal is output through a device, when an average volume of the audio signal does not satisfy a volume threshold for a specified timespan, determining a difference between the average volume and a desired volume, and applying a gain to the audio signal to adjust the volume of the audio signal to the desired volume, the gain determined based on the difference between the average volume and the desired volume.