Auditory Context Detection for Adaptive Mobile Audio Recording
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Solution Overview
Problem
Existing audio and speech signal capturing technologies in mobile devices lack the ability to dynamically adjust recording parameters based on auditory context, leading to suboptimal audio quality and inefficient power consumption.
Innovation Solution
A smart audio logging system that processes digital audio signals to determine auditory context information, dynamically adjusting parameters such as microphone usage, sampling frequency, signal enhancement, and storage location to enhance audio quality and optimize power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual initiation and termination of recording is used, then device complexity is reduced, but ease of operation deteriorates due to lack of automation
Solution Approach 1:
The system automatically detects auditory context information and initiates or terminates recording without user intervention. The processor monitors audio signals, identifies relevant contexts (such as speech, music, or noise patterns), and autonomously controls recording operations, making the system serve itself rather than requiring manual user commands.
Solution Approach 2:
The system performs preliminary analysis of auditory context information before initiating recording. By pre-processing and evaluating audio signals to determine whether recording conditions are met, the system prepares and decides on recording actions in advance, enabling automatic start/termination without complex real-time decision-making during operation.
2Adaptability or versatility
If fixed operational parameters are used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system dynamically adjusts operational parameters including sampling frequency, microphone selection, and audio enhancement levels based on real-time auditory context analysis. When speech is detected, the system may prioritize voice-optimized microphones and sampling rates; when music is detected, it may switch to higher fidelity recording modes, making the system adaptable to different recording scenarios.
Solution Approach 2:
The system changes operational parameters based on detected audio context. The processor modifies sampling frequency, bit depth, and microphone array configuration according to the type of sound being recorded (speech, music, ambient noise), enabling optimal recording quality for different acoustic environments without manual intervention.
3Manufacturing precision
If continuous high-quality audio enhancement is applied, then audio quality is improved, but power consumption increases
Solution Approach 1:
The system applies audio enhancement periodically rather than continuously. The processor evaluates auditory context at intervals and activates enhancement algorithms only when relevant audio events are detected (such as speech or music). During periods of low audio activity or when recording ambient noise, enhancement is reduced or suspended, conserving power while maintaining quality when needed.
Solution Approach 2:
The system applies partial audio enhancement based on the detected context. Instead of always applying maximum enhancement, the processor selectively applies enhancement algorithms appropriate to the current audio scenario (e.g., noise suppression for speech, frequency enhancement for music), using only the necessary processing power for each situation rather than excessive continuous enhancement.
4Measurement precision
If context analysis is performed continuously, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system performs context analysis periodically rather than continuously. The processor samples audio signals at specific intervals and evaluates auditory context at these discrete moments, achieving sufficient detection accuracy without the continuous computational burden of constant analysis. This periodic sampling reduces power consumption while maintaining effective context awareness.
Solution Approach 2:
The system uses a multi-functional processor that handles both audio enhancement and context analysis tasks. By integrating these functions into a single processing unit rather than using separate dedicated hardware for each function, the system achieves comprehensive audio monitoring and analysis while reducing overall power consumption through shared computational resources.
Data Source
AI summary
A device includes a memory configured to store a captured audio input signal and one or more processors configured to process the captured audio input signal to determine auditory context information within the captured audio input signal. The one or more processors are configured to determine an audio quality enhancement level to be applied to the captured audio input signal based on the determined auditory context information, and perform audio quality enhancement on the captured audio input signal based on the determined audio quality enhancement level, wherein the audio quality enhancement level is dynamically adjusted during the storing of the captured audio input signal according to the determined auditory context information.