Audio Signal Normalization Using Loudness Metadata and Peak Estimation
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Solution Overview
Problem
Conventional audio processing methods for loudspeaker systems often result in negative audio artifacts such as clipping, distortion, and dynamic range compression artifacts, failing to maintain consistent loudness levels and user-defined sound preferences.
Innovation Solution
A method and system utilizing loudness metadata and machine learning models to estimate peak-level amplitudes, determining content-adaptive gains for audio signals, ensuring consistent loudness and avoiding artifacts during playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional audio processing methods are used for loudspeaker systems, then audio processing functionality is provided, but negative audio artifacts such as clipping, distortion, and dynamic range compression artifacts occur
Solution Approach 1:
The system extracts loudness metadata and estimates peak-level amplitudes before audio playback to determine appropriate gain scaling. This preliminary analysis allows the system to pre-calculate normalization gains based on actual content characteristics, preventing clipping and distortion before they occur during playback.
Solution Approach 2:
The system dynamically adjusts gain parameters based on estimated peak-level amplitudes derived from loudness metadata. By changing the gain parameter adaptively rather than using fixed gain scaling, the system maintains consistent loudness perception while avoiding audio artifacts across different content types.
2Ease of operation
If fixed gain scaling is used for audio normalization, then processing simplicity is maintained, but consistent loudness perception across different audio content types cannot be achieved
Solution Approach 1:
The system automatically extracts loudness metadata and estimates peak-level amplitudes from the audio content itself, allowing the audio signal to inform its own normalization requirements. This self-service approach eliminates the need for manual gain setting while achieving consistent loudness perception across different content types.
Solution Approach 2:
The system transitions from fixed gain scaling to dynamic gain determination by estimating peak-level amplitudes from loudness metadata. This parameter change enables adaptive normalization that maintains loudness consistency while requiring minimal user intervention.
3Reliability
If loudness metadata and machine learning models are used to estimate peak-level amplitudes, then consistent loudness perception is achieved, but processing complexity increases
Solution Approach 1:
The system uses loudness metadata as an intermediary representation that captures essential audio characteristics without requiring full analysis of the audio signal. This intermediary data structure enables peak-level amplitude estimation through machine learning models, achieving accurate loudness normalization while keeping processing complexity manageable.
4Object-generated harmful factors
If gain is determined based on estimated peak-level amplitude, then audio artifacts are minimized, but additional processing steps are required
Solution Approach 1:
The system performs peak-level amplitude estimation and gain determination in advance of audio playback. By completing these processing steps preliminarily, the system minimizes audio artifacts during actual playback while the additional processing occurs in advance when it does not affect audio quality.
Data Source
AI summary
One embodiment provides a method of signal normalization. The method comprises receiving an input content with a corresponding audio signal, and extracting loudness metadata from an audio signal corresponding to the input content. The method further comprises estimating, using a machine learning model, a peak-level amplitude based on the loudness metadata. The peak-level amplitude represents a maximum linear amplitude of the audio signal over an entire duration of the input content. The method further comprises determining a gain based at least on the peak-level amplitude, and applying the gain to the audio signal. The resulting gain-scaled audio signal is provided to one or more speakers coupled to or integrated in an electronic device for audio playback.