Audio Stem Masking Analysis Using Loudness Loss Modeling
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Solution Overview
Problem
Conventional methods for addressing sound masking in audio mixes are imprecise, error-prone, and labor-intensive, relying heavily on user recognition and trial-and-error to identify and correct frequency ranges where masking occurs.
Innovation Solution
The system models sound masking as a function of energy and relative energy between audio stems, using psychoacoustic models to compute loudness and partial loudness, and identifies frequency ranges with significant loudness loss to enable systematic and efficient correction through graphical user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional trial-and-error methods are used to identify and correct sound masking, then users can eventually achieve desired audio quality, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the manual trial-and-error mechanical process with an automated computational system that uses psychoacoustic models and signal processing algorithms to automatically identify and correct sound masking, eliminating the need for iterative manual adjustment
Solution Approach 2:
The system performs self-analysis by automatically computing loudness loss, identifying masking frequency ranges, and suggesting corrective equalization parameters without requiring user intervention or subjective listening tests
2Measurement precision
If users manually identify frequency ranges with sound masking through listening tests, then they can detect masking issues, but the detection becomes error-prone and imprecise
Solution Approach 1:
The patent replaces subjective human listening and manual frequency identification with objective computational analysis using psychoacoustic models that precisely calculate loudness loss and identify masking frequency ranges algorithmically
Solution Approach 2:
The system provides objective feedback through computed loudness loss values and visualizations that show exactly which frequency ranges are affected by masking, replacing subjective user perception with measurable data
3Measurement precision
If comprehensive audio analysis is performed to accurately identify sound masking across all frequency ranges, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the audio frequency spectrum into multiple discrete frequency ranges and analyzes each range separately using individual psychoacoustic models, allowing comprehensive analysis to be broken down into manageable computational segments
Solution Approach 2:
The system changes analysis parameters by computing loudness and partial loudness for each frequency range independently, allowing precise measurement without requiring a single complex all-encompassing analysis model
Data Source
AI summary
Some embodiments of the invention are directed to enabling a user to easily identify the frequency range(s) at which sound masking occurs, and addressing the masking, if desired. In this respect, the extent to which a first stem is masked by one or more second stems in a frequency range may depend not only on the absolute value of the energy of the second stem(s) in the frequency range, but also on the relative energy of the first stem with respect to the second stem(s) in the frequency range. Accordingly, some embodiments are directed to modeling sound masking as a function of the energy of the stem being masked and of the relative energy of the masked stem with respect to the masking stem(s) in the frequency range, such as by modeling sound masking as loudness loss, a value indicative of the reduction in loudness of a stem of interest caused by the presence of one or more other stems in a frequency range.