Mood Profile Determination For Media Data
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Solution Overview
Problem
Conventional methods for managing media data, such as audio recordings, lack sufficient detail and dimension for dynamic tasks like recommendation and matching, as they rely on characteristics like genre and release date, which do not capture the emotional or mood-related aspects of the content.
Innovation Solution
A system and method for determining a mood profile of media data, including audio recordings, by extracting low- and mid-level features and comparing them to mood classification models to assign mood categories and scores, allowing for the identification of congruent mood profiles and recommendations based on mood similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional characteristics like genre and release date are used for media data classification, then the classification system is simple and easy to implement, but the detail and dimension of description are insufficient for dynamic tasks like recommendation and matching
Solution Approach 1:
The mood profile is segmented into multiple discrete mood categories (e.g., happy, sad, angry, calm) with associated confidence scores. This segmentation allows the system to capture nuanced emotional dimensions without requiring a single complex classification model, thereby increasing information detail while managing system complexity through modular category definitions
Solution Approach 2:
The patent adds a new dimensional layer (mood/emotional dimension) to the traditional media classification system. By introducing mood categories and confidence scores as an additional dimension alongside conventional characteristics like genre and release date, the system enriches the description space without completely replacing the existing simple classification structure
2Loss of information
If mood profiles with multiple categories and confidence scores are computed for media recordings, then the description detail and dimension are significantly improved, but the computational complexity and processing requirements increase
Solution Approach 1:
Mood profiles are pre-computed and stored with media recordings during ingestion or indexing phases. This preliminary action allows the computationally intensive mood analysis to be performed in advance rather than in real-time during playback or recommendation, reducing the power requirements during actual media consumption and recommendation operations
Solution Approach 2:
The patent uses audio fingerprints or identifiers as keys to retrieve pre-computed mood profiles from a database. Instead of re-computing mood profiles for each comparison operation, the system creates and stores copies of mood profile data that can be efficiently retrieved and compared, significantly reducing computational power requirements during runtime operations
3Adaptability or versatility
If mood profiles are used for dynamic tasks like recommendation and matching, then the adaptability and personalization capability are enhanced, but the data processing and comparison operations become more complex
Solution Approach 1:
The system allows dynamic adjustment of confidence score thresholds and mood category weights to adapt to different user preferences and contexts. By changing parameters like the minimum confidence threshold for mood matching or the relative importance of different mood categories, the system can adapt its recommendation behavior without requiring complex algorithmic changes, thus enhancing adaptability while managing processing complexity
Data Source
AI summary
An example method involves comparing a primary element of a first piece of audio data to a primary element of a second piece of audio data; based on the comparing of the primary elements, determining that the first and second pieces of audio data have the same predominant mood category; in response to determining that the first and second pieces of audio data have the same predominant mood category, comparing a first mood score of the primary element of the first piece of audio data to a second mood score of the primary element of a second piece of audio data; determining that an output of the comparison of the two mood scores exceeds a threshold value; and in response to determining that the output of the comparison of the two mood scores exceeds the threshold value, providing an indicator to an application.