Automatic Speaker Equalization Using Room Position Sensing
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Solution Overview
Problem
Manual audio equalization is cumbersome and requires advanced knowledge, making it difficult for average users to achieve high-quality audio output in varying room environments without repeated setup.
Innovation Solution
Electronic devices with integrated microphones automatically equalize audio output by analyzing phase differences and room characteristics using machine learning, eliminating the need for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual audio equalization is performed, then audio output quality can be improved, but user time and effort are significantly consumed
Solution Approach 1:
The system uses the device's own microphones to automatically measure room acoustic characteristics and perform equalization without requiring external measurement microphones or user intervention. The device self-calibrates by analyzing audio signals captured during normal operation.
Solution Approach 2:
The system automatically adjusts equalization parameters (frequency response, gain) based on measured room characteristics. Machine learning models process acoustic measurements and generate appropriate equalization curves to compensate for room effects.
2Manufacturing precision
If manual audio equalization is performed, then audio output quality can be improved, but the process becomes too complex for average users
Solution Approach 1:
The system performs complete automatic equalization using built-in microphones and machine learning algorithms, eliminating the need for users to understand acoustic measurement procedures or manually adjust equalization settings.
Solution Approach 2:
The patent replaces manual mechanical adjustment processes with automated electronic measurement and processing. Machine learning models automatically analyze acoustic data and generate equalization parameters without user intervention.
3Power
If speakers are placed near room boundaries, then low frequency output is reinforced, but this creates boomy bass that degrades audio quality
Solution Approach 1:
The system applies preliminary equalization adjustments to counteract the expected reinforcement of low frequencies when speakers are placed near boundaries. The machine learning model predicts boundary effects and pre-compensates by reducing bass gain in anticipation of the reinforcement.
Solution Approach 2:
The system uses feedback from microphones capturing audio signals to measure actual room response. The measured frequency response is used to generate equalization curves that compensate for boundary-induced bass reinforcement, creating a closed-loop control system.
4Manufacturing precision
If room equalization is performed manually, then audio quality can be optimized for a specific room, but the process must be repeated whenever room or speaker placement changes
Solution Approach 1:
The system enables dynamic re-equalization by continuously or periodically measuring room acoustic characteristics using built-in microphones. When speaker placement or room configuration changes, the system automatically detects the new acoustic environment and applies appropriate equalization without requiring user intervention.
Solution Approach 2:
The machine learning model processes acoustic measurements and dynamically adjusts equalization parameters based on detected room characteristics. The system adapts to different placements by changing frequency response parameters in real-time.
Data Source
AI summary
The various implementations described herein include methods, devices, and systems for automatic audio equalization. In one aspect, a method is performed at an electronic device that includes speakers, microphones, processors and memory. The electronic device outputs audio user content from the speakers and automatically equalizes subsequent audio output of the device without user input. The automatic equalization includes: (1) obtaining audio content signals, including receiving outputted audio content at each microphone; (2) determining from the audio content signals phase differences between microphones; (3) obtaining a feature vector based on the phase differences; (4) obtaining a frequency correction from a correction database based on the obtained feature vector; and (5) applying the obtained frequency correction to the subsequent audio output.