Streaming Audio Transformation for Text-Based Content Matching
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Solution Overview
Problem
Existing systems for selecting additional media content based on non-textual streaming media, such as podcasts, are inefficient and resource-intensive due to reliance on inaccurate or incomplete context data, leading to unnecessary consumption of network bandwidth, processing power, and memory usage.
Innovation Solution
Transform non-textual streaming media content into textual form, analyzing audio characteristics like speaker distinction and emphasis to enhance context understanding, enabling text-based content selection systems to identify relevant additional content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text-based systems use manually provided context data for content selection, then the system is simple to operate, but the accuracy of content matching deteriorates due to inaccurate or incomplete context data
Solution Approach 1:
The patent introduces an audio analysis intermediary system that processes streaming media audio content and generates enhanced context data. This intermediary layer analyzes audio characteristics, speaker distinctions, and emphasis patterns to produce accurate textual representations that bridge the gap between non-textual audio content and text-based matching systems, thereby improving content selection accuracy without complicating the overall system operation
Solution Approach 2:
The patent replaces manual context data provision with automated audio analysis technology. Instead of relying on manual metadata creation, the system uses speech-to-text conversion, audio characteristic analysis, and speaker differentiation algorithms to automatically generate accurate context data, substituting mechanical manual processes with automated technical systems
2Measurement precision
If the system streams additional media content to improve content selection, then the accuracy of content recommendation improves, but network bandwidth consumption increases
Solution Approach 1:
The patent performs preliminary audio analysis and context extraction from streaming media before content matching occurs. By pre-processing the audio content to extract speaker identities, emphasis patterns, and contextual information, the system prepares accurate matching data in advance, enabling precise content recommendation without needing to stream additional media content for analysis
Solution Approach 2:
The patent extracts essential contextual information from audio content by separating and analyzing specific audio characteristics such as speaker voice patterns, emphasis indicators, and speech content. This extraction process isolates the critical matching features from the full audio stream, enabling accurate content selection without requiring the system to process or stream the entire media content
3Measurement precision
If the system processes non-textual streaming media directly, then content selection accuracy improves, but processing power and memory usage increase
Solution Approach 1:
The patent substitutes direct processing of non-textual audio data with text-based processing systems. By converting audio content to textual representations through speech-to-text technology and audio characteristic analysis, the system enables text-based matching algorithms to process the content, significantly reducing processing power and memory requirements while maintaining content selection accuracy
Solution Approach 2:
The patent transforms audio data from its original non-textual form into textual parameters and features that can be processed by existing text-based systems. This parameter transformation includes converting speech to text, extracting audio characteristics as numerical features, and representing speaker distinctions as categorical data, thereby enabling efficient processing with reduced computational resources
4Productivity
If the system distributes additional content based on incomplete context data, then resource distribution is faster, but inappropriate content is distributed wasting resources
Solution Approach 1:
The patent implements feedback mechanisms where audio analysis results continuously inform content selection decisions. By analyzing audio characteristics, speaker distinctions, and contextual patterns in real-time, the system receives feedback about the actual content being streamed and uses this information to dynamically adjust content recommendations, ensuring appropriate content distribution while maintaining fast processing speeds
Solution Approach 2:
The patent performs preliminary audio analysis and context extraction before content distribution decisions are made. By pre-processing audio content to extract accurate contextual information, speaker identities, and emphasis patterns, the system prepares reliable matching data in advance, enabling fast and accurate content distribution without wasting resources on inappropriate recommendations
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating augmented content streams by transforming non-textual content into a form that enables a text-based matching system to select non-textual content are described. In some aspects, a method includes obtaining first audio, storing a text transcription of the first audio in a searchable database. Media content that includes second audio is obtained. The second audio is transformed into textual content. A determination is made, based on a search of the searchable database, that the textual content of the second audio matches the text transcription of the first audio. The first audio is inserted into the media content to create an augmented content stream in response to the determination that the textual content of the second audio matches the text transcription of the first audio.