Neural Audio Quality Assessment for Adaptive Equalization Filtering
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Solution Overview
Problem
Conventional audio equalization methods fail to address nuances between different songs and require manual adjustment, often processing media with poor audio quality, which wastes computing resources and does not improve listener experience.
Innovation Solution
A neural network-based system dynamically adjusts equalization settings in real-time by analyzing audio signals using a constant-Q transform representation and a trained EQ neural network, determining optimal settings based on a library of reference media profiles, and discarding filters with minimal impact on audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional audio equalization methods are used, then manual adjustment is required and computing resources are wasted on poor-quality media, but audio quality enhancement and automation are achieved through neural network-based dynamic adjustment
Solution Approach 1:
The system performs preliminary analysis of audio signals using constant-Q transform before applying equalization filters. The neural network is pre-trained on reference media profiles to predict optimal equalization settings in advance, enabling automatic adjustment without manual intervention while maintaining audio quality
Solution Approach 2:
The neural network system autonomously analyzes audio characteristics, determines quality metrics, and adjusts equalization filters without requiring manual user input. The system self-regulates by comparing predicted settings against reference profiles and automatically applying corrections
2Productivity
If all audio signals are processed regardless of quality, then no quality filtering is applied, but computing resources are efficiently conserved by discarding poor-quality media
Solution Approach 1:
The system extracts and identifies poor-quality audio signals from the input stream using neural network-based quality assessment. These low-quality signals are separated and discarded before further processing, preventing waste of computational resources on media that cannot be meaningfully enhanced
Solution Approach 2:
Instead of processing all audio signals equally, the system applies selective processing only to high-quality signals that meet predetermined thresholds. This partial action approach concentrates computational resources on signals where equalization will actually improve audio quality
3Ease of operation
If manual equalization adjustment is required, then user control is maintained, but user convenience and real-time adaptation are improved through automated neural network-based settings
Solution Approach 1:
The system dynamically adjusts equalization filters in real-time based on the neural network's analysis of each audio signal's characteristics. The equalization settings are not fixed but adapt continuously as different media are played, providing real-time optimization without requiring manual user reconfiguration
Solution Approach 2:
The neural network receives feedback from constant-Q transform analysis of the audio signal and continuously refines equalization predictions. The system uses reference media profiles as feedback benchmarks to determine optimal filter settings, automatically adapting to different genres and audio characteristics
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to determine audio quality. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal. Example apparatus disclosed herein also include an EQ analyzer to access a plurality of equalization settings determined by the neural network based on the query; and compare the equalization settings to an equalization threshold to determine if the audio signal is to be removed from subsequent processing.