Dynamic Audio Playback Equalization with Semantic Features
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Solution Overview
Problem
Existing audio playback systems fail to dynamically adjust equalization based on high-level semantic characteristics of media content and contextual information, relying instead on low-level physical features or metadata that may not accurately represent the content or environment.
Innovation Solution
A computer-based system that utilizes feature vectors representing semantic characteristics of media content and contextual information from auxiliary sensors to determine dynamic frequency response profiles for optimal audio playback, incorporating user preferences and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual equalizer setting is used, then user can adjust levels for each frequency, but it is troublesome and requires user manipulation for each piece of music
Solution Approach 1:
The system automatically analyzes the audio signal and applies appropriate equalization without requiring user intervention. The equalizer settings are determined by the system itself based on the musical content, eliminating the need for manual adjustment while maintaining optimal sound quality for different music genres and playback scenarios.
2Ease of operation
If pre-set equalizer list is used, then user manipulation is reduced, but it still requires user selection and may not accurately represent individual media content properties
Solution Approach 1:
The system dynamically changes equalization parameters based on real-time analysis of the audio signal's spectral characteristics. By continuously monitoring frequency content, tempo, and other audio features, the system automatically selects and adjusts equalization settings to precisely match the actual content being played, rather than relying on static pre-sets or metadata.
3Extent of automation
If automatic equalization based on metadata is used, then user manipulation is eliminated, but audio files are adjusted by manually associated metadata that may not be a true representation of individual media content properties
Solution Approach 1:
The system replaces metadata-based equalization with direct audio signal analysis. Instead of relying on externally associated metadata that may be inaccurate or generic, the system performs spectral analysis and feature extraction directly from the audio signal itself, enabling precise automatic equalization that accurately reflects the actual acoustic properties of each individual media content item.
4Reliability
If psychoacoustic-based equalization is used, then low frequency compensation is achieved, but it does not consider high-level semantic characteristics or playback context
Solution Approach 1:
The system extends equalization from traditional psychoacoustic frequency compensation to include multiple additional dimensions: semantic content analysis (genre, mood, instrumentation), playback context (environmental noise, device type, user preferences), and temporal dynamics. This multi-dimensional approach allows the system to adapt equalization settings comprehensively based on both the audio content characteristics and the playback situation.
Data Source
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AI summary
A method and system for optimizing audio playback by dynamically equalizing an audio signal (1), using an associated feature vector (2) comprising feature values (3) representing semantic characteristics of the audio signal (1), for determining a frequency response profile (4) and applying the frequency response profile (4) to the audio signal (1) to produce an equalized audio signal (7) for playback through an audio interface (26).