Masking Sound Selection by Ambient Noise Frequency Analysis
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Solution Overview
Problem
Existing masking sound technologies require manual selection of masking sounds based on noise types, which is cumbersome and inefficient.
Innovation Solution
A masking sound selection device that identifies noise types based on frequency characteristics of ambient sounds and automatically selects masking sounds associated with those noise types for optimal masking effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection of masking sounds is implemented, then user can choose appropriate masking sounds, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically identifies noise types from ambient sound and selects appropriate masking sounds without user intervention. The noise type identification unit detects characteristics of surrounding noises, and the masking sound selection unit autonomously chooses and outputs corresponding masking sounds, enabling the system to serve itself rather than requiring manual user configuration.
Solution Approach 2:
The patent replaces manual mechanical selection with automated acoustic analysis. Frequency characteristic analysis units analyze the spectral properties of ambient noises, and control units process this data to automatically select masking sounds based on predefined relationships between noise types and effective masking sounds, substituting human judgment with computational analysis.
2Reliability
If masking sound information is stored for each noise type, then masking effectiveness improves, but device complexity increases
Solution Approach 1:
The system segments noise types based on their frequency characteristics into distinct categories (e.g., low-frequency noises, mid-frequency noises, high-frequency noises). Each noise type is associated with specific masking sounds stored in the storage unit. This segmentation allows the system to match appropriate masking sounds to specific noise categories, improving masking effectiveness while organizing the complexity into manageable segments.
Solution Approach 2:
The system changes the parameter of noise classification from requiring detailed spectral analysis to using broader frequency range categorization. By defining noise types based on frequency ranges (low, mid, high) rather than detailed spectral signatures, the system reduces the complexity of the storage requirements and selection logic while maintaining effective masking through frequency-based matching.
Data Source
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AI summary
[Problem] To provide a technology with which it is possible to automatically select a masking sound with a high masking effect for ambient noise and the like. [Solution] A wireless speaker 1 comprises: a masking sound information storage unit 15 in which the name of a masking sound is stored for each noise type; a sound collection unit 16 that collects an ambient sound; a frequency characteristic analysis unit 17 that analyzes the frequency characteristic of audio data of the ambient sound collected by the sound collection unit 16; and a masking sound selection unit 18 that, on the basis of the frequency characteristic analyzed by the frequency characteristic analysis unit 17, specifies the noise type of the ambient sound. The masking sound selection unit 18 selects, as a masking sound to be superimposed on the ambient sound, a masking sound specified by the name stored in the masking sound information storage unit 15 in association with the specified noise type.