Audio Sound Level Measurement Using Hash-Based Loudness Matching
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Solution Overview
Problem
Users face inconvenience due to varying reference sound levels in audio content across different suppliers, requiring a method to accurately measure and adjust sound levels for a comfortable listening experience.
Innovation Solution
A method and device using a hash address database to measure and adjust sound levels by cumulatively counting hash addresses corresponding to sound levels, identifying average sound levels over time, and applying compensation gains to maintain uniform sound levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sound level measurement is performed manually by users, then users can adjust sound levels according to their preferences, but users have to re-adjust sound levels each time content changes which is inconvenient
Solution Approach 1:
The system automatically measures sound levels of audio content and adjusts them without user intervention. The sound level measurement device autonomously receives audio signals, measures their sound levels, and applies compensation gains to maintain consistent sound levels across different content, eliminating the need for manual user adjustment.
Solution Approach 2:
The system continuously measures the sound level of incoming audio content and compares it against a reference level. Based on this feedback, it automatically calculates and applies compensation gains to adjust the sound level, creating a closed-loop control system that maintains consistent audio output without user intervention.
2Reliability
If sound levels of different audio content are measured and adjusted, then consistent sound quality can be achieved, but the system becomes more complex
Solution Approach 1:
The sound level measurement process is divided into discrete segments: receiving audio signals, converting them to spectral components via FFT, identifying peak frequencies, measuring sound levels at these peaks, and applying compensation gains. This segmentation simplifies the overall complex task into manageable, modular steps that can be implemented systematically.
Solution Approach 2:
The system introduces intermediate processing steps between raw audio input and final output: FFT conversion to spectral domain, peak frequency identification, and sound level measurement at specific frequencies. These intermediaries simplify the measurement process by focusing on critical frequency components rather than analyzing the entire audio spectrum directly.
3Measurement precision
If detailed sound level measurement is performed across multiple frequencies, then measurement precision improves, but processing time increases
Solution Approach 1:
Instead of uniformly measuring sound levels across all frequencies, the system identifies peak frequencies where audio content actually exists and concentrates measurement efforts only at these local regions. This approach maintains measurement precision by focusing on relevant frequency components while significantly reducing processing time by ignoring frequencies without audio content.
Solution Approach 2:
The system performs sound level measurement only at necessary peak frequencies rather than across the entire frequency spectrum. This partial action approach achieves sufficient measurement precision for audio content reproduction while avoiding the excessive processing time that would result from comprehensive full-spectrum analysis.
Data Source
AI summary
Provided are a method and a device for identifying a sound level of audio content, the method including receiving an audio signal and obtaining a first hash address based on a sound level of the audio signal, providing a hash address database in which the sound level of the audio signal is made to correspond to a hash address among a plurality of hash addresses, based on a first sound level corresponding to the first hash address and the sound level of the audio signal corresponding to the first sound level, cumulatively counting a first count value of the first hash address, and identifying the sound level of the audio content received during a first time period based on count information corresponding to a total amount of the first count value accumulated during the first time period.