Audio Equalization Profiles for Automatic Genre-Based Tuning
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Solution Overview
Problem
Current audio systems lack automated equalization settings that adapt to different types of audio content, leading to subpar listening experiences when switching between genres such as podcasts and music.
Innovation Solution
An audio system that automatically adjusts equalization settings based on the type of audio being broadcast by performing frequency analysis, generating spectral plots, and matching them to predetermined settings files, or using metadata to retrieve optimized equalizer settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tone profiles are provided for different audio types, then users can select appropriate settings for specific genres, but the system requires continuous user intervention and cannot automatically adapt when switching between audio types
Solution Approach 1:
The system automatically detects audio genre and applies appropriate equalization settings without user intervention. The processor analyzes audio characteristics and self-adjusts the equalizer settings based on the detected genre, eliminating the need for manual profile selection while maintaining adaptability across different audio types
Solution Approach 2:
The system dynamically changes equalization parameters (frequency bands, gain values) based on detected audio genre characteristics. By monitoring audio signal properties and automatically adjusting equalization parameters, the system adapts to different genres while removing the need for manual user input
2Ease of operation
If a single set of equalization settings is used for all audio types, then the system operates simply without genre detection, but the listening experience becomes subpar when switching between different audio genres
Solution Approach 1:
The system transitions from static equalization settings to dynamic settings that automatically adjust based on real-time audio genre detection. The equalizer parameters change dynamically according to the detected audio characteristics, maintaining system simplicity while achieving optimized audio quality for each genre
Solution Approach 2:
The system pre-establishes multiple equalization profiles for different audio genres and automatically selects the appropriate profile based on real-time audio analysis. This preliminary preparation of multiple settings allows the system to maintain simplicity while delivering optimized quality for each genre through automatic selection
3Extent of automation
If automated genre detection and equalization adjustment is implemented, then the system provides optimized settings without user intervention, but the device complexity increases with frequency analysis and spectral plot generation
Solution Approach 1:
The system extracts only the essential audio characteristics needed for genre identification from the full audio signal, rather than processing all spectral data. By focusing on key frequency components and temporal patterns, the system achieves automated genre detection with reduced processing complexity
Solution Approach 2:
The system performs partial frequency analysis focusing on specific frequency ranges and characteristics most indicative of audio genre, rather than complete spectral analysis. This selective approach enables automated equalization adjustment while limiting processing complexity to only the necessary analysis
4Manufacturing precision
If tone profiles are optimized for specific genres like podcasts or music, then audio quality is maximized for that genre, but the settings become incompatible when switching to different audio types
Solution Approach 1:
The system creates a universal equalization system that handles multiple audio genres through a single automated detection mechanism. By implementing genre-agnostic detection algorithms that identify audio characteristics regardless of type, the system provides optimized settings for each genre while maintaining versatility across all audio types through one unified system
Data Source
AI summary
Disclosed herein is a system and method for optimizing audio equalization settings based on the type of music being broadcast, comprising: receiving audio at a processing device; performing a frequency analysis of the received audio, to determine a plurality of frequency components and associated amplitudes of the frequency components of the received audio; generating a spectral plot of the received audio based on the performed frequency analysis of the received audio, wherein the spectral plot comprises an x axis that represents relative amplitude and a y axis that represents frequency components of the received audio; comparing the spectral plot of the received audio to each of a plurality of different predetermined spectral plots; matching the spectral plot of the received audio to at least one of a plurality of different predetermined spectral plots; retrieving a predetermined equalizer settings file that corresponds to the at least one of the matched predetermined spectral plots, wherein the predetermined equalizer settings file comprises a plurality of relative amplitude settings for each of a plurality of frequency bands; and applying the retrieved predetermined equalizer settings to an equalizer that processes the received audio according to the predetermined equalizer settings in the predetermined equalizer settings file.