ML Sound Detection Alerts for Deaf Users
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deaf and hard of hearing individuals face challenges in determining whether someone is speaking to them or identifying specific sounds in their vicinity due to limited or absent audio cues, and existing alert devices are often limited to specific purposes and not portable or adaptable to different sound frequencies and environments.
Innovation Solution
A computer system utilizing a machine learning classification model trained on user-provided sound recordings and indicators to identify sound types, enabling alerts via visual, tactile, or haptic notifications on mobile or wearable devices, which can be used anywhere and adapt to different sound patterns across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional home-based alerting devices are used for specific purposes, then reliability for that specific function is improved, but adaptability to different environments and sound frequencies deteriorates
Solution Approach 1:
The patent implements a universal sound detection system that can identify multiple types of sounds (fire alarms, ambulance sirens, doorbells, baby cries, speech) using a single device. The machine learning model is trained on diverse sound recordings from various environments and frequency ranges, enabling the system to adapt to different sound types without requiring separate dedicated devices for each function.
Solution Approach 2:
The system changes its detection parameters dynamically by adjusting the machine learning model's sensitivity and frequency range based on the detected sound characteristics. The model can adapt to different sound frequencies and patterns encountered in various environments, allowing the same device to reliably detect different types of sounds across diverse conditions.
2Adaptability or versatility
If multiple dedicated alerting devices are used for different purposes, then coverage of different sound types is improved, but device complexity and portability deteriorate
Solution Approach 1:
The patent merges the functionality of multiple dedicated alerting devices into a single integrated system. By combining various sound detection capabilities (fire alarm detection, siren detection, speech detection, etc.) into one device with a universal machine learning model, the system eliminates the need for users to carry or manage multiple separate devices while maintaining comprehensive coverage of different sound types.
Solution Approach 2:
A single device is designed to perform multiple sound detection functions through a versatile machine learning model that can identify and classify different sound types. This universal approach allows one device to replace multiple specialized devices, reducing complexity and improving portability while maintaining broad adaptability.
3Reliability
If traditional alerting devices are used, then they provide reliable alerts for specific purposes at home, but portability to different locations deteriorates
Solution Approach 1:
The system transitions from static, location-fixed alerting devices to a dynamic, portable solution. The machine learning model can adapt to different acoustic environments and sound patterns encountered at various locations, maintaining reliable detection performance whether the user is at home, in public spaces, or traveling. The device's ability to learn and adjust to different environments ensures consistent reliability across diverse locations.
Data Source
AI summary
Custom alerts may be generated based on sound type indicators determined using a machine learning classification model trained on user-provided sound recordings and user-defined sound type indicators. A device may provide a sound recording and a type indicator identifying an entity that made a sound in the recording for storage in a database that includes a plurality sound recordings associated with a plurality of type indicators. A machine learning classification model may be trained based on the stored recordings, including the user-defined recordings. The model may be used to classify sounds recorded by other devices and generate alerts identifying the type of sound. Thus, multiple users may contribute data to customize machine learning models that recognize sounds and generate alerts based on user-defined identifiers.