Personal Audio System Cloud-Based Sound Knowledgebase
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Solution Overview
Problem
Existing sound processing technologies fail to effectively tailor ambient sound to individual preferences, particularly in varying environments, as they do not adequately address the need for frequency attenuation, amplification, noise reduction, and augmentation while considering location-based and context-specific conditions.
Innovation Solution
A sound processing system comprising active acoustic filters and a sound knowledgebase that uses wireless communication protocols to process ambient sound, select appropriate filter parameters based on location, ambient sound profiles, and user context, and dynamically adjust sound characteristics to enhance listening experiences by integrating personal audio systems with cloud-based data and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If active acoustic filters are used to attenuate unwanted frequencies and amplify desired sounds, then sound quality and user satisfaction are improved, but device complexity increases due to integration of cloud-based data and machine learning algorithms
Solution Approach 1:
The patent introduces a cloud-based sound knowledgebase as an intermediary that stores pre-processed sound profiles and filter parameters. The personal audio system queries this external database rather than maintaining all processing capabilities locally, thereby improving sound quality through sophisticated filtering while reducing local device complexity.
Solution Approach 2:
The system performs sound profile analysis and filter parameter optimization in advance, storing results in the cloud knowledgebase. When a user needs sound modification, the pre-computed parameters are retrieved and applied directly, eliminating the need for real-time complex processing in the personal audio device.
2Adaptability or versatility
If the system dynamically adjusts sound characteristics based on location and ambient sound profiles, then adaptability is improved, but loss of time increases due to data retrieval and processing from cloud
Solution Approach 1:
The system pre-processes ambient sound profiles and determines optimal filter parameters in advance, storing these in the cloud knowledgebase with associated location and environmental metadata. When the personal audio system needs adaptation, it simply queries and retrieves pre-determined parameters rather than performing real-time analysis.
Solution Approach 2:
The system creates simplified representations (copies) of complex sound environments and their optimal processing parameters, storing these in the cloud knowledgebase. The personal audio system then retrieves and applies these copied parameter sets, avoiding the need to recreate the full analysis process each time.
3Measurement precision
If machine learning algorithms are used to learn processing parameters from user feedback, then user preference accuracy is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent introduces a cloud-based sound knowledgebase as an intermediary that handles complex machine learning operations. The personal audio system collects user feedback and transmits it to the cloud, where algorithms learn and refine processing parameters. The refined parameters are then retrieved and applied locally, achieving high user preference accuracy while keeping personal device complexity low.
Solution Approach 2:
The system performs the computationally intensive machine learning process in advance in the cloud environment, storing learned processing parameters in the knowledgebase. The personal audio system then retrieves these pre-learned parameters, avoiding the need to run complex algorithms on the limited hardware of personal audio devices.
4Manufacturing precision
If the system processes and transmits sound data to cloud for collective feedforward, then sound modification quality is improved, but loss of energy increases due to wireless communication and data transmission
Solution Approach 1:
The system extracts and transmits only the essential data elements needed for cloud processing—such as ambient sound profiles, location information, and user feedback—rather than transmitting complete audio streams. This selective data extraction maintains sound modification quality while minimizing the energy required for wireless communication.
Data Source
AI summary
Learning personal audio systems and methods are disclosed. A learning personal audio system characterizes the digitized ambient sound so as to generate feature data for the digitized ambient sound, instructs a network interface to transmit the feature data to the remote server, requests one or more appropriate sound profiles from the remote server based upon the feature data, receives one or more selected sound profiles, selected by the remote server based upon the feature data, and initiates processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound. The one or more selected sound profiles for the learning personal audio system are based upon sound profiles which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.