Audio Data Processing for Recommended Segment Identification
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Solution Overview
Problem
Current methods for identifying recommended segments in multimedia data, especially for newly released audio/video content, rely heavily on manual annotation and subjective feelings, leading to inefficiencies and inconsistencies due to the lack of playback record data.
Innovation Solution
A method that extracts audio track data from multimedia content, allocates weight values based on signal source types, fuses attention parameters with weight values to highlight important segments, and predicts recommendation parameters to accurately identify recommended segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to identify recommended segments, then annotation accuracy can be maintained, but annotation efficiency is low and cannot achieve fast batch production
Solution Approach 1:
The patent replaces manual annotation with an automated audio analysis system that uses deep learning models to automatically identify recommended segments. The system extracts audio features, generates attention parameter sequences through encoding, and determines recommendation parameters without human intervention, thereby achieving both high accuracy and efficient batch processing
Solution Approach 2:
The patent transforms manual annotation into an automated parameter-based system by extracting audio features and generating recommendation parameters through mathematical operations. The system uses weight value sequences, attention parameter sequences, and fusion parameters to objectively determine recommended segments, replacing subjective manual judgment with quantifiable parameters
2Loss of information
If manual annotation is used to identify recommended segments, then detailed annotation can be provided, but the process is time-consuming and subjective feelings dominate the annotation results
Solution Approach 1:
The patent substitutes manual annotation with automated audio feature extraction and analysis systems that process audio data through deep learning models. The system automatically identifies recommended segments by analyzing audio characteristics, eliminating time-consuming manual processes while maintaining comprehensive annotation coverage
Solution Approach 2:
The system performs self-service analysis by automatically extracting audio features, generating attention parameters, and determining recommendation parameters without requiring human annotators. The automated system serves itself by processing audio data through integrated algorithms that objectively identify recommended segments based on audio characteristics
3Productivity
If only frequency domain analysis is used to identify recommended segments, then processing speed can be maintained, but identification comprehensiveness is insufficient
Solution Approach 1:
The patent extends analysis from single frequency domain to dual domain approach by incorporating time domain analysis through weight value sequences and attention parameter sequences. This dimensional expansion allows the system to capture temporal patterns and signal source characteristics while maintaining frequency domain insights, achieving comprehensive identification without significant speed penalty
Data Source
AI summary
A method of media processing includes extracting audio track data for at least a signal source type from audio data. The audio data includes multiple data segments, the audio track data includes at least a time period that is determined to be related to the signal source type. The method further includes allocating weight values respectively to the data segments in the audio data according to the audio track data, concatenating the weight values to form a weight value sequence of the audio data, extracting audio features respectively from the data segments, concatenating the audio features of the data segments to form an audio feature sequence of the audio data, encoding the audio feature sequence to obtain an attention parameter sequence of the audio data, fusing the attention parameter sequence and the weight value sequence to obtain fusion parameters respectively for the data segments, and determining recommendation parameters accordingly.