Ambient Noise Masking Using Mood-Aware Multi-Device Audio Control
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Solution Overview
Problem
Ambient background noise from various devices in homes can be distracting and undesirable, as existing technologies lack effective methods to dynamically modify noise levels based on user mood and behavior.
Innovation Solution
A system that determines sound output capabilities of devices, monitors ambient noise levels, and adjusts device operations to create a desired noise profile based on user feedback and mood/behavior information, using multiple data sources to recommend actions that impact the user's emotional state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If devices emit sounds or noise as primary function or byproduct, then device functionality is achieved, but user distraction and unwanted noise increase
Solution Approach 1:
The system converts harmful ambient noise from devices into beneficial masked noise by capturing device sounds and replaying them through audio outputs at controlled levels. This transforms the original harmful distraction into a useful tool for masking other unwanted sounds, improving user focus while maintaining device functionality.
Solution Approach 2:
The noise management system acts as an intermediary between noise-emitting devices and the user. It captures sounds from devices, processes them through an audio system, and outputs modified versions that mask other distracting noises. This intermediary layer allows device functionality to continue while eliminating the harmful distraction effect.
2Object-affected harmful factors
If ambient noise levels are increased to mask distracting sounds, then user focus is improved, but overall noise pollution increases
Solution Approach 1:
The system applies noise masking locally and selectively rather than uniformly increasing all ambient noise. It identifies specific distracting sounds and targets those frequencies and time periods with masked noise, leaving other portions of the acoustic environment unchanged. This localized approach improves focus without creating general noise pollution.
Solution Approach 2:
The system dynamically adjusts noise masking parameters including volume level, frequency spectrum, and temporal distribution based on real-time analysis of ambient sounds and user context. By changing these parameters adaptively, the system provides effective masking only when and where needed, avoiding unnecessary noise generation.
3Object-affected harmful factors
If noise management system continuously monitors and adjusts device operations, then desired noise profile is achieved, but system complexity increases
Solution Approach 1:
The noise management system performs self-service by automatically monitoring ambient noise levels, analyzing device operations, and adjusting audio outputs without requiring continuous user intervention. The system autonomously maintains the desired noise profile through feedback loops, reducing the need for complex user interfaces and manual configuration.
Solution Approach 2:
The system integrates multiple functions into a single noise management platform: monitoring ambient noise, capturing device sounds, processing audio signals, controlling audio outputs, and analyzing user context. This multi-functionality consolidates what could be separate complex systems into one unified solution, managing noise profile control efficiently.
4Object-affected harmful factors
If device operations are adjusted to achieve desired noise profile, then ambient noise is optimized, but device functionality may be compromised
Solution Approach 1:
The system performs preliminary actions by capturing and storing device sounds before they become problematic distractions. It pre-processes these sounds and prepares masked noise versions in advance, allowing rapid response when distractions occur without needing to adjust device operations in real-time, thus preserving device functionality.
Solution Approach 2:
Instead of adjusting device operations to reduce noise output, the system inverts the approach by capturing the device sounds and replaying them through dedicated audio outputs. This inversion allows ambient noise optimization without affecting device operations, as the noise control is handled separately through the audio system.
Data Source
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AI summary
Methods, systems, and media for ambient background noise modification are provided. In some implementations, the method comprises: identifying at least one noise present in an environment of a user having a user device, an activity the user is currently engaged in, and a physical or emotional state of the user; determining a target ambient noise to be produced in the environment based at least in part on the identified noise, the activity the user is currently engaged in, and the physical or emotional state of the user; identifying at least one device associated with the user device to be used to produce the target ambient noise; determining sound outputs corresponding to each of the one or more identified devices, wherein a combination of the sound outputs produces an approximation of one or more characteristics of the target ambient noise; and causing the one or more identified devices to produce the determined sound outputs.