Distributor Quality Scoring Through Audio File Quality Analysis
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Solution Overview
Problem
Existing audio file distribution services lack a standardized method to objectively evaluate and compare the quality of audio files, leading to inconsistent service quality across different distributors.
Innovation Solution
A method and system for calculating a distributor quality score by sending audio files to user terminals, adding a distributor identifier, analyzing the files to generate quality indicators, and calculating a score based on these indicators in a rating server, which can aggregate and compare scores across different regions and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple audio files are sent and analyzed to calculate distributor quality scores, then the measurement precision and reliability of quality evaluation is improved, but the loss of time and processing resources increases
Solution Approach 1:
The system pre-calculates and stores quality indicators for audio files when they are first received from distributors. These pre-computed indicators are then reused for multiple distributors, eliminating the need to re-analyze the same audio files repeatedly. This preliminary action significantly reduces processing time while maintaining measurement precision.
Solution Approach 2:
The system creates copies of audio files and their associated quality indicators, distributing them to multiple user terminals and distributors. Instead of having each distributor independently analyze the same files, the system replicates the analysis results across the network, reducing redundant processing and saving time while preserving evaluation accuracy.
2Reliability
If human users add audio quality flags in metadata, then the reliability of quality assessment is improved, but the ease of operation decreases
Solution Approach 1:
The system automatically generates quality indicators by analyzing audio files themselves, without requiring human users to manually add quality flags. The analysis module extracts quality metrics directly from the audio content and metadata, making the system self-sufficient and eliminating the need for user intervention while maintaining reliable quality assessment.
Solution Approach 2:
The system replaces the manual mechanical process of users adding quality flags with an automated digital analysis process. The analysis module uses computational methods to evaluate audio quality, substituting human judgment with objective algorithmic assessment that is both reliable and operationally simple.
3Reliability
If blockchain is used to store metadata hashes for detecting modifications, then the reliability of metadata integrity verification is improved, but the device complexity increases
Solution Approach 1:
The system uses blockchain as an intermediary layer to store and verify metadata hashes. Rather than implementing a complex custom verification system, the patent leverages the existing blockchain infrastructure to provide reliable integrity checking. The blockchain acts as a neutral, trusted mediator that simplifies the verification process while maintaining high reliability.
4Measurement precision
If multiple file quality indicators are generated for each audio file, then the measurement precision of distributor quality score is improved, but the loss of information processing increases
Solution Approach 1:
The system extracts and focuses on the most relevant quality indicators from audio files, separating essential quality metrics from unnecessary data. By identifying and extracting only the key indicators needed for accurate distributor evaluation, the system maintains measurement precision while reducing the overall information processing load.
Solution Approach 2:
The system applies different quality indicator generation strategies based on local conditions - for example, generating detailed indicators for files from distributors with poor track records while using simpler indicators for consistently high-quality distributors. This localized approach optimizes measurement precision where needed while reducing processing load elsewhere.
Data Source
AI summary
A method and system are provided for calculating a distributor quality score. At least one audio file is sent from a distributor server to at least one user terminal. In each of the at least one user terminal, a distributor identifier is added in the metadata of each of the at least one audio file. For each of the at least one audio file, the audio file is analyzed in order to generate at least one file quality indicator. In a rating server, a distributor quality score is calculated based on the at least one file quality indicator.