Audio Loudness Control Using Specific Loudness Target Matching
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Solution Overview
Problem
Current methods for measuring and controlling perceived sound loudness and spectral balance in audio signals are inadequate, as they fail to accurately reflect subjective impressions, especially in complex sounds and environments with background noise, due to their inability to account for frequency and level variations.
Innovation Solution
The development of a system that modifies audio signals to reduce the difference between their specific loudness and a target loudness, using psychoacoustic models and filters to simulate the human hearing process, allowing for time- and frequency-smoothing to achieve perceptually accurate control of loudness and spectral balance, even in noisy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional loudness measurement methods are used, then the measurement process is simple, but the measurement precision is insufficient to accurately reflect subjective impressions of loudness and spectral balance
Solution Approach 1:
The audio spectrum is divided into multiple frequency bands corresponding to critical bands of human hearing. The system calculates specific loudness for each band separately using psychoacoustic models, then integrates these to obtain total loudness. This segmentation approach improves measurement accuracy by accounting for frequency-dependent hearing sensitivity while maintaining manageable computational complexity through structured processing of individual bands.
Solution Approach 2:
The system transforms the audio signal into the perceptual domain by applying psychoacoustic transformations that convert physical acoustic parameters into perceived loudness parameters. This includes calculating excitation patterns, specific loudness, and using non-linear compression functions to map physical intensity to perceived loudness in sone units, thereby achieving accurate subjective loudness measurement.
2Manufacturing precision
If psychoacoustic models are applied to control specific loudness, then the perceived loudness control accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system pre-calculates critical band filters and psychoacoustic transformation parameters before processing the audio signal. By preparing the perceptual model structures and filter banks in advance, the system reduces real-time computational complexity while maintaining accurate specific loudness control through the pre-established psychoacoustic framework.
Solution Approach 2:
The psychoacoustic model automatically adapts to the input signal characteristics by self-adjusting the critical band filtering and excitation pattern calculations based on the signal's spectral content. This self-adaptive mechanism achieves accurate perceived loudness control without requiring manual tuning or complex external control systems.
3Manufacturing precision
If frequency-dependent loudness control is implemented, then the spectral balance accuracy is improved, but the ease of operation decreases
Solution Approach 1:
The system continuously monitors the specific loudness across frequency bands and uses feedback to adjust the audio signal, applying frequency-dependent gain adjustments that maintain accurate spectral balance. The feedback loop compares the measured specific loudness against target values and automatically corrects deviations, achieving precise spectral control without requiring manual intervention.
Data Source
AI summary
Audio signal processing relating to the measurement and control of the perceived sound loudness and/or the perceived spectral balance of an audio signal is useful, for example, in one or more of: loudness-compensating volume control, automatic gain control, dynamic range control (including, for example, limiters, compressors, expanders, etc.), dynamic equalization, and compensating for background noise interference in an audio playback environment. In various embodiments, modification parameters are derived for modifying the audio signal in order to reduce the difference between its specific loudness and a target specific loudness.