Context-Driven Media Classification for Faster Query Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing media classification systems are computationally expensive and time-consuming when performing audio fingerprinting on large databases, especially when identifying ambient media content in various environments.
Innovation Solution
A system that uses context information to select optimized classification models, such as convolutional neural networks, for efficient media classification, reducing computational costs by leveraging context parameters like location and device information to narrow down classification models and improve media type probability indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio fingerprinting is used to classify media content, then comprehensive media identification is achieved, but computational expense and processing time increase significantly
Solution Approach 1:
The patent segments the media classification process into multiple stages: first using lightweight context parameters (device type, location, time) to identify probable media types, then applying only the necessary spectral analysis and classification models for those specific types. This segmentation avoids comprehensive audio fingerprinting for all media, reducing computational expense while maintaining identification accuracy.
Solution Approach 2:
The patent performs preliminary classification using context parameters before conducting detailed spectral analysis. By pre-identifying the likely media type based on device context, the system prepares targeted classification models in advance, avoiding the need to process all possible media types equally and thus reducing overall computational burden.
2Measurement precision
If comprehensive spectral analysis is performed on all media queries, then accurate media type classification is achieved, but processing time increases
Solution Approach 1:
The patent applies local quality by using different analysis depths for different media types. Context parameters determine which spectral features to extract and which classification models to apply. For example, audio queries might use full spectral analysis while image queries use different features entirely, optimizing processing time for each media type's specific requirements.
Solution Approach 2:
The patent dynamically adjusts the classification approach based on context parameters. The system selects and applies only the relevant classification models needed for the identified media type, rather than statically applying all possible models to every query. This dynamic adaptation reduces processing time while maintaining classification accuracy.
3Measurement precision
If multiple classification models are applied to all queries, then media classification accuracy is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal context parameter framework that works across all media types and devices. This single contextual analysis system serves multiple functions: identifying device type, location, time, and query characteristics, then using this information to select appropriate classification models. This multi-functional approach reduces overall system complexity compared to having separate classification systems for each media type.
Solution Approach 2:
The patent introduces context parameters as an intermediary layer between the raw media query and the classification models. This intermediary processes the query characteristics and device context to determine which models to apply, simplifying the overall system architecture by avoiding direct connections between all possible query types and all possible models.
Data Source
AI summary
A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that a particular received query corresponds to a known media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, classifying a media query includes accessing digital media data and a context parameter from a first device. A model for use with the network-based classifier system can be selected based on the context parameter. In an example embodiment, the network-based classifier system provides a media type probability index for the digital media data using the selected model and spectral features corresponding to the digital media data. In an example embodiment, the digital media data includes an audio or video signal sample.