Audio Equalization Preference Learning From Listener Feedback
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Solution Overview
Problem
Audio production tools are complex and require technical expertise, making it difficult for novice users to achieve desired sound modifications, as they lack understanding of how to manipulate parameters to achieve specific perceptual effects such as 'bright' or 'warm' sounds, leading to inefficiencies and miscommunication between users and audio engineers.
Innovation Solution
A method and system for listener calibration that generates a weighting function based on user feedback, allowing users to map language-based descriptors to frequency-gain curves, enabling novice users to adjust audio signals without direct manipulation of equalizer controls, by correlating user ratings with frequency band gains and applying these settings to achieve desired sound modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional equalizer interfaces with technical parameters are used, then manufacturing precision and measurement precision are improved, but ease of operation deteriorates for novice users
Solution Approach 1:
The patent introduces language-based descriptors (e.g., 'bright', 'warm') as an intermediary layer between the user's perceptual goals and the technical equalizer parameters. Users interact with intuitive descriptors rather than directly manipulating frequency and gain parameters, while the system automatically translates these descriptors into appropriate equalization settings based on learned mappings from audio examples.
Solution Approach 2:
The patent replaces the mechanical interaction of directly adjusting equalizer sliders and parameters with an automated computational system. Instead of manually positioning controls to achieve desired sound characteristics, users provide linguistic feedback about their perceptual goals, and the system computationally determines the optimal parameter settings through pattern recognition and machine learning algorithms.
2Ease of operation
If language-based descriptors are used for sound modification, then ease of operation is improved, but manufacturing precision and measurement precision deteriorate
Solution Approach 1:
The patent implements feedback loops where the system presents audio examples with specific equalization settings to the user, collects linguistic feedback about the perceived sound characteristics, and uses this feedback to refine and update the mappings between descriptors and parameters. This iterative feedback process allows the system to learn precise relationships between user language and technical settings, improving measurement precision over time while maintaining ease of operation.
Solution Approach 2:
The patent enables the system to automatically learn and establish the relationships between language descriptors and equalizer parameters through self-service machine learning processes. The system autonomously analyzes user feedback, identifies patterns in the data, and generates optimized parameter mappings without requiring manual programming or calibration by technicians, thereby maintaining precision while simplifying user interaction.
3Adaptability or versatility
If individualized preference learning is implemented, then adaptability is improved, but loss of time increases due to case-by-case calibration
Solution Approach 1:
The patent performs preliminary action by pre-processing and analyzing user feedback during initial calibration sessions to build comprehensive preference models. The system collects and processes linguistic responses, identifies individual user patterns, and stores learned mappings for rapid retrieval and application in future sessions, eliminating the need for repeated time-consuming calibration procedures while maintaining high adaptability to individual preferences.
Solution Approach 2:
The patent creates universal mappings between language descriptors and equalizer parameters that can be applied across different users, sound types, and scenarios. By learning common patterns in user language and sound preferences, the system develops a reusable framework that adapts to individual users through minimal calibration while maintaining broad applicability, thereby reducing calibration time without sacrificing personalization.
4Adaptability or versatility
If the number of equalization curves explored is increased, then adaptability is improved, but loss of time becomes prohibitively large
Solution Approach 1:
The patent extracts the essential characteristics of user preference from a small set of carefully selected audio examples and linguistic responses. Instead of requiring users to evaluate numerous equalization curves, the system identifies and extracts the key perceptual dimensions and parameter relationships that define user preferences, then applies these extracted insights to generate appropriate settings for new sounds, achieving high adaptability with minimal calibration time.
Solution Approach 2:
The patent changes the approach from exploring many equalization curves to learning the underlying parameter relationships that define user preferences. The system transforms the problem from brute-force curve exploration to parameter-based modeling, where understanding the relationships between frequency bands, gain adjustments, and perceptual descriptors enables efficient generation of appropriate equalization settings without requiring extensive user evaluation of multiple curves.
Data Source
AI summary
Systems, methods, and apparatus are provided for equalization preference learning for digital audio modification. A method for listener calibration of an audio signal includes modifying a reference sound using at least one equalization curve; playing the modified reference sound for a listener; accepting listener feedback regarding the modified reference sound; and generating a weighting function based on listener feedback. A listener audio configuration system includes an output providing a sound for listener review; an interface accepting listener feedback regarding the sound; and a processor programming an audio device based on listener feedback.