Dynamic Equalization Target Profiles via Audio Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dynamic equalization (DEQ) techniques require manual selection of target profiles, which can lead to perceptual degradation of audio content due to inappropriate choices, especially when multiple reference audio content items are available.
Innovation Solution
Automatically generate and select target profiles for DEQ by clustering reference audio content items based on audio characteristics and semantic labels, using techniques like k-means clustering and Euclidean distance metrics to ensure appropriate profile selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection of target profiles is used, then user control and flexibility are improved, but the risk of perceptual degradation increases due to inappropriate profile choices
Solution Approach 1:
The system automatically generates multiple target profiles from reference audio content and performs self-selection by computing similarity metrics between input audio and available profiles, eliminating the need for manual user selection while ensuring appropriate profile matching through algorithmic comparison of audio characteristics
Solution Approach 2:
The system transforms the discrete choice problem into a continuous parameter space by computing similarity scores based on audio features (spectral centroid, zero-crossing rate, RMS energy), allowing automatic selection of the optimal target profile through parameter comparison rather than manual category selection
2Adaptability or versatility
If automatic generation of multiple target profiles is implemented, then the system can provide better matching for diverse audio content, but the computational complexity and processing time increase
Solution Approach 1:
The system pre-generates multiple target profiles from reference audio content during an offline preparation phase, organizing them into a reusable library with pre-computed audio features. This preliminary action eliminates the need for real-time profile generation during actual DEQ operations, reducing online computational complexity while maintaining adaptability
Solution Approach 2:
The system segments the audio processing task into distinct phases: (1) offline profile generation from reference content, (2) feature extraction from input audio, (3) similarity computation between input and profiles, and (4) gain application. This segmentation allows complex operations to be distributed across time and simplifies real-time processing
3Measurement precision
If clustering techniques are used to organize reference audio content, then the automatic selection accuracy is improved, but the computational resources required for clustering increase
Solution Approach 1:
Instead of performing complete clustering on the entire reference audio library, the system extracts only the most discriminative audio features (spectral centroid, zero-crossing rate, RMS energy) and uses these partial features for similarity computation. This partial action achieves sufficient selection accuracy without the computational cost of full clustering
Solution Approach 2:
The system uses lightweight, computationally inexpensive similarity metrics (Euclidean distance on extracted features) rather than expensive clustering algorithms during runtime. These simple distance computations serve as disposable, fast approximations that provide sufficient accuracy for automatic profile selection without requiring heavy computational resources
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
In an embodiment, a method comprises: filtering reference audio content items to separate the reference audio content items into different frequency bands; for each frequency band, extracting a first feature vector from at least a portion of each of the reference audio content items, wherein the first feature vector includes at least one audio characteristic of the reference audio content items; obtaining at least one semantic label from at least a portion of each of the reference audio content items; obtaining a second feature vector consisting of the first feature vectors per frequency band and the at least one semantic label; generating, based on the second feature vector, cluster feature vectors representing centroids of clusters; separating the reference audio content items according to the cluster feature vectors; and computing an average target profile for each cluster based on the reference audio content items in the cluster.