Media Stream Customizer for Buffer Management and Mood Consistency
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Solution Overview
Problem
Conventional methods for maintaining buffered media streams often result in abrupt transitions in theme or mood during playback due to random or user-preference-based insertion of media objects, leading to discontinuities as objects are deleted from the stream.
Innovation Solution
A media stream customizer that analyzes the stream and a library of media objects to select and insert objects based on similarity, using pitch-preserving audio stretching and compression to maintain buffer levels and minimize discontinuities, ensuring smooth transitions by choosing objects that match the surrounding content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If media objects are deleted from the buffered media stream, then user preference or random insertion methods are used to maintain buffer levels, but this causes abrupt transitions and discontinuities in theme or mood during playback
Solution Approach 1:
The system continuously monitors the buffered media stream to identify media objects and their characteristics (genre, tempo, mood). When a deletion occurs, the system uses this feedback information to select a replacement object that matches the surrounding content's characteristics, ensuring smooth transitions and maintaining thematic consistency while replenishing the buffer.
Solution Approach 2:
The system changes the selection parameters for media object insertion from random or simple user preference-based criteria to similarity-based criteria that consider multiple parameters such as genre, tempo, mood, and contextual compatibility. This ensures that inserted objects match the surrounding content and maintain the overall theme or mood of the stream.
2Stability of the object's composition
If similarity-based selection is used to insert media objects, then smooth transitions are achieved, but the complexity of the system increases due to analysis requirements
Solution Approach 1:
The system performs preliminary analysis and characterization of media objects (identifying genre, tempo, mood, and other characteristics) as they are buffered, before any deletions occur. This pre-computed information is stored and readily available when a deletion happens, allowing for rapid similarity-based selection without complex real-time analysis during the critical insertion moment.
Solution Approach 2:
The system uses the existing metadata and characteristics of the buffered media stream itself to perform the similarity analysis, rather than requiring external complex analysis systems. The media objects' own properties (genre, tempo, mood information) are leveraged to find matches, making the system self-sufficient and reducing external complexity.
Data Source
AI summary
A “media stream customizer” customizes buffered media streams by inserting one or more media objects into the stream to maintain an approximate buffer level. Specifically, when media objects such as songs, jingles, advertisements, etc., are deleted from the buffered stream (based on some user specified preferences), the buffer level will decrease. Therefore, over time, as more objects are deleted, the amount of the media stream being buffered continues to decrease, thereby limiting the ability to perform additional deletions from the stream. To address this limitation, the media stream customizer automatically chooses one or more media objects to insert back into the stream, and ensures that the inserted objects are consistent with any surrounding content of the media stream, thereby maintaining an approximate buffer level. In addition, the buffered content can also be stretched using pitch preserving audio stretching techniques to further compensate for deletions from the buffered stream.