Segment-Aware Audio Component Volume Adjustment for Media Playback
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Solution Overview
Problem
Existing media playback systems fail to adjust the volume of individual audio components beyond dialogue and background noise, neglecting the nuanced audio components within a media asset.
Innovation Solution
A media guidance application determines the type of a media segment and parses individual audio components, using metadata and crowd-sourced data to identify categories and subcategories, adjusting volume parameters based on user preferences and environmental context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the audio signal is uniformly adjusted into background noise and dialogue, then the basic audio separation is achieved, but the individual audio components beyond dialogue and background noise cannot be adjusted
Solution Approach 1:
The audio signal is segmented into multiple components including dialogue, background noise, music, sound effects, and ambient noise. Each segment can be independently adjusted, allowing fine-grained control over individual audio elements while maintaining the overall audio structure.
Solution Approach 2:
Different audio components are assigned different quality levels and adjustment parameters. The system applies localized processing to each audio segment, enabling selective volume adjustment and quality optimization for specific components based on user preferences and scene context.
2Measurement precision
If individual audio components are parsed and categorized, then precise volume control is enabled, but the system complexity increases
Solution Approach 1:
A neural network serves as an intermediary between the audio signal and the classification database. The neural network processes the audio signal, extracts features, and maps them to corresponding categories in the database, enabling accurate identification without direct complex analysis of all audio parameters.
Solution Approach 2:
The system creates a database of pre-defined audio categories and their characteristics. Instead of analyzing every possible audio variation from scratch, the system compares parsed audio components against this database of templates, enabling efficient and accurate classification through pattern matching.
3Adaptability or versatility
If volume parameters are adjusted based on segment type metadata, then the audio adjustment becomes adaptive to content, but the system requires additional metadata processing
Solution Approach 1:
Metadata about segment types (action scenes, dialogue scenes, music scenes, etc.) is prepared and stored in advance alongside the audio content. This preliminary organization allows the system to quickly retrieve and apply appropriate volume parameters without performing complex analysis during playback.
Solution Approach 2:
The system uses feedback from the segment type classification to adjust volume parameters dynamically. The neural network analyzes the audio, identifies the segment type, retrieves corresponding volume parameters from the database, and applies them in real-time, creating a closed-loop adaptive system.
Data Source
AI summary
Systems and methods are provided herein for determining whether to adjust volumes of individual audio components in a media asset based on a type of segment of the media asset that is playing back. A media guidance application may determine that a user is playing back a segment of a media asset. The media guidance application may determine a type corresponding to the segment. The media guidance application may parse a plurality of audio components of the media asset that are playing back during the segment. The media guidance application may determine, for each audio component, whether to adjust the volume playing back during the segment based on the type. For each audio component of the plurality of audio components, in response to determining to adjust the volume, the media guidance application may adjust the volume of the audio component playing back during the segment.