Sound Analysis for Personalized Audio Output
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Solution Overview
Problem
Existing electronic devices fail to provide personalized and context-aware sound outputs in response to user-generated sounds, such as humming or playing an instrument, as they do not consider the diversity of users or contexts, resulting in unsatisfactory music experiences.
Innovation Solution
An electronic device equipped with a microphone, processor, and memory that analyzes input sounds to determine the type of output sound and context, generating appropriate background sounds or feedback, using play modes and user preferences to customize the output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If related-art electronic devices provide uniform information output for all sound inputs, then the device operation is simple, but the user experience lacks personalization and context-awareness
Solution Approach 1:
The sound analysis process is segmented into distinct components: voice activity detection, pitch detection, and context analysis. Each segment handles a specific aspect of the input sound, allowing the system to process complex audio data through manageable stages rather than requiring a single monolithic analysis mechanism.
Solution Approach 2:
The system performs preliminary voice activity detection and pitch detection before generating the final output. By pre-processing the sound input to identify speech presence and pitch characteristics, the device prepares contextual information in advance that guides the subsequent personalized response generation.
2Adaptability or versatility
If the device analyzes sound to determine context and user preferences, then the output becomes personalized and context-aware, but the processing time increases
Solution Approach 1:
The system performs partial analysis by focusing on specific sound characteristics (voice activity presence, pitch range) rather than comprehensive audio analysis. This selective approach extracts only the necessary contextual information needed for personalized output while avoiding unnecessary processing of other sound attributes.
Solution Approach 2:
The patent replaces complex mechanical or computational analysis systems with simplified detection mechanisms. Instead of using elaborate methods to determine user context, the system substitutes straightforward voice activity detection and pitch detection algorithms that deliver sufficient contextual information with minimal processing overhead.
3Reliability
If the device provides detailed feedback on user singing or playing, then the learning value increases, but the system complexity increases
Solution Approach 1:
The system extracts only the essential feedback elements needed for user improvement: pitch accuracy comparison and contextual appropriateness evaluation. By isolating and providing only these critical feedback components rather than comprehensive audio analysis, the system maintains feedback accuracy while limiting system complexity to what is truly necessary.
Data Source
AI summary
An electronic device includes a microphone, a speaker, a processor operatively connected to the microphone and the speaker, and a memory electrically connected to the processor and storing instructions that, when executed by the processor, cause the processor to receive a sound through the microphone, analyze the received sound, determine a song associated with the sound, and a kind of output sound based on at least in part on a result of the analyzing, generate an output sound based on the kind of output sound, and output the generated output sound through the speaker.