Audio Clip Selection via Machine Learning and User Feedback
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Solution Overview
Problem
Conventional social networking systems often provide users with random and undesirable portions of audio tracks for inclusion in content items, as they rely on audio producers to designate fixed time segments without considering the audio content, leading to suboptimal user experience.
Innovation Solution
A social networking system uses a machine-learned model to identify desirable portions of audio tracks based on audio content analysis, such as changes in frequency and user feedback, to suggest relevant audio clips for content customization, ensuring users can easily add audio to their content items without extensive searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If audio producers designate fixed time segments in audio tracks, then the system can provide audio clips to users, but the audio clips may be random and undesirable as they do not consider audio content
Solution Approach 1:
The patent replaces the manual mechanical process of audio producers designating fixed time segments with an automated machine-learned model that analyzes audio content. The model automatically identifies desirable portions based on acoustic features, tempo, and user feedback, eliminating the need for manual selection while improving selection quality.
Solution Approach 2:
The system enables itself to select audio clips by implementing a machine-learned model that autonomously analyzes audio tracks and identifies desirable portions. The model learns from user feedback and continuously improves its selection capability without requiring external intervention from audio producers.
2Manufacturing precision
If users manually search for desirable audio portions, then they can find contextually relevant audio clips, but it requires extensive time and effort
Solution Approach 1:
The system performs preliminary analysis of audio tracks before users need them. The machine-learned model pre-identifies desirable portions of audio tracks based on content analysis, so when users need audio clips, ready-to-use segments are already prepared and can be immediately provided without requiring user search or manual selection.
Solution Approach 2:
The patent introduces a machine-learned model as an intermediary between the audio track and the user. This intermediary automatically analyzes the audio content, identifies desirable portions, and presents them to users, eliminating the need for users to manually search through entire audio tracks while ensuring high relevance to the content.
3Ease of operation
If the system provides predefined audio clips, then users can easily add audio to content items, but the functionality remains limited and does not allow customization
Solution Approach 1:
The patent implements a dynamic audio selection system where the machine-learned model adapts its selections based on user feedback and usage patterns. The system evolves from providing static predefined clips to dynamically customized selections that learn from user preferences, allowing both ease of operation and adaptability to be achieved simultaneously.
Solution Approach 2:
The system incorporates user feedback loops where user selections and interactions with provided audio clips are analyzed to continuously refine the machine-learned model. This feedback mechanism allows the system to maintain ease of operation by automatically providing clips while simultaneously improving adaptability and customization based on learned user preferences.
Data Source
AI summary
Techniques for selecting portions of audio tracks for content item inclusion are described. For example, a social networking system may receive, from an audio producer, an audio track. In some examples, the social networking system may, using a machine-learned model, determine a first portion of the audio track having a change of frequency over time, and provide the first portion of the audio track to a first user for inclusion in a content item. In some cases, the social networking system may receive a selection of a second portion of the audio track. Based on the selection of the second portion of the audio track, the social networking system may modify the parameters of the machine-learned model to provide the second portion of the audio track to a second user.