Semantic Audio Equalization for Content-Specific Playback
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Solution Overview
Problem
Existing methods for dynamic audio equalization of media content fail to account for high-level semantic characteristics and contextual information, leading to suboptimal playback experiences as they rely on low-level physical features and manual user adjustments.
Innovation Solution
A computer-implemented method that uses high-level feature vectors to determine frequency response profiles for audio signals, allowing for automatic and dynamic equalization based on semantic characteristics and contextual factors, without requiring additional feature extraction steps or manual user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual equalizer setting is used, then user can adjust frequency levels, but it is troublesome and requires user manipulation for each piece of music
Solution Approach 1:
The system automatically analyzes audio features and determines equalizer settings without user intervention. The processor extracts features from the audio signal, selects appropriate equalizer parameters based on these features, and applies the equalization automatically, making the system serve itself rather than requiring manual user configuration for each track
Solution Approach 2:
Equalizer settings are pre-calculated and stored based on audio feature analysis. When a track is played, the system retrieves and applies the pre-determined settings rather than requiring real-time manual adjustment or complex real-time analysis, preparing the equalization parameters in advance
2Ease of operation
If pre-set equalizer lists are used, then user manipulation is reduced, but it still requires user selection and does not adequately represent individual media content properties
Solution Approach 1:
Instead of applying a single pre-set equalizer configuration to all tracks, the system analyzes the specific audio features of each individual track and determines customized equalizer settings tailored to that specific content. Each piece of media receives localized, content-specific equalization rather than a generic pre-set
Solution Approach 2:
The system dynamically adjusts equalizer parameters based on extracted audio features such as tempo, rhythm, and spectral characteristics. The equalizer settings are not fixed but are modified according to the specific parameters of each audio track, enabling precise adaptation to individual content properties
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 which may not be a true representation of individual media content properties
Solution Approach 1:
The system replaces manual metadata association with automatic audio feature extraction. Instead of relying on mechanically assigned metadata tags that may be inaccurate, the processor directly analyzes the audio signal to extract features such as tempo, rhythm, and spectral characteristics, substituting the manual metadata mechanism with an automated analysis system that derives content properties directly from the audio itself
Solution Approach 2:
The audio content itself provides the information needed for equalization through automatic feature extraction. The system analyzes the intrinsic properties of each audio track and uses these self-derived features to determine equalizer settings, rather than relying on externally assigned metadata that may not accurately represent the content
4Extent of automation
If equalization based on low-level physical features is used, then dynamic automatic equalization is achieved, but contextual information and content mood cannot be taken into account
Solution Approach 1:
The system transitions from analyzing only low-level physical audio features to incorporating high-level semantic and contextual dimensions. By extracting features such as tempo, rhythm, and spectral characteristics that reflect the musical content and mood, the system adds semantic dimensionality to the equalization process, enabling it to consider both physical and contextual properties of the audio
Data Source
AI summary
A method and system for optimizing audio playback by dynamically equalizing an audio signal, using an associated high-level feature vector with high-level feature values representing semantic characteristics of the audio signal, for determining a frequency response profile and applying the frequency response profile to the audio signal to produce an equalized audio signal for playback through an audio interface.