Automatic Representative Media Clip Selection via Audio Feature Analysis

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Solution Overview

Problem

Current methods for selecting representative clips for electronic media, such as songs, either rely on human operators or simple rules, which are inefficient and may not capture the most appealing parts of songs, especially those with long intros or significant differences between sections.

Innovation Solution

A system that collects user interaction data and analyzes audio features to automatically identify human recognizable labels and select a representative clip based on positive user interactions and audio characteristics, using a combination of machine learning techniques to deconstruct and label audio streams.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If human operators select representative clips from each song, then the quality and appeal of representative clips is improved, but the scalability and productivity deteriorate due to multiple languages and thousands of songs released weekly

Engineering Contradiction:

Improvequality of representative clip selectionVSAvoidscalability of clip selection process

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system enables automatic selection of representative clips through machine learning models that analyze audio features and user interaction data without human intervention. The algorithm independently identifies memorable portions by detecting audio features such as tempo changes, frequency patterns, and structural transitions, then selects clips based on aggregated user behavior data including replay counts and pause locations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of human operators listening to and selecting clips is replaced with an automated computational system. Machine learning models process audio streams and interaction data to automatically identify representative portions, substituting human cognitive and manual work with algorithmic analysis and automated decision-making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If simple rules are used to select the first thirty seconds of songs, then the productivity and ease of operation is improved, but the quality and appeal of representative clips deteriorates, especially for songs with long intros or significant differences between sections

Engineering Contradiction:

Improveefficiency of clip selection processVSAvoidquality and appeal of representative clip

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system dynamically adjusts clip selection parameters based on analyzed audio features and user interaction patterns. Instead of fixed time-based rules, the algorithm identifies variable temporal boundaries by detecting structural transitions, tempo changes, and frequency patterns that mark memorable portions of songs, adapting the selection criteria to each individual track's characteristics.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The clip selection process transitions from static rule-based timing to dynamic, adaptive selection. The system continuously learns from user interaction data and adjusts its identification of memorable portions based on actual user behavior patterns, making the selection process flexible and responsive rather than rigid and predetermined.

Inventive Principle:

Principle #15Dynamics

3Productivity

If automated systems are implemented to select representative clips, then the productivity and scalability is improved, but the complexity of the system increases due to machine learning models and data collection infrastructure

Engineering Contradiction:

Improvescalability of clip selectionVSAvoidsystem complexity for automated selection

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated system is divided into distinct functional modules: audio feature extraction components that analyze temporal, spectral, and rhythmic patterns; user interaction data collection and aggregation systems; machine learning models that process features and predict memorable portions; and clip selection algorithms that generate final outputs. Each module operates independently with well-defined interfaces, enabling modular development and maintenance.

Inventive Principle:

Principle #1Segmentation

4Measurement precision

If user interaction data is collected and analyzed, then the accuracy of representative clip selection is improved, but the loss of user privacy and increase in data processing requirements worsens

Engineering Contradiction:

Improveaccuracy of clip selectionVSAvoiduser privacy

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The collected user interaction data serves multiple functions: it trains the machine learning models to identify memorable portions, validates the accuracy of selected clips, and enables continuous improvement of the selection algorithm. The same data infrastructure supports both model training and performance evaluation, maximizing the utility of collected information while minimizing redundant data collection.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS7826911B1Automatic selection of representative media clips

Publication Date: 2010.11.02 GOOGLE LLC

AI summary

A system determines human recognizable labels for portions of an electronic media stream, gathers data associated with the electronic media stream from a number of media players, and determines at least one section of the electronic media stream with a particular media feature. The system selects a representative clip for the electronic media stream based on information regarding the labeled portions, the gathered data, and the at least one section.