Music Mashup Recommendation Tool Using Tagged Audio Links
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Solution Overview
Problem
Users face difficulties in identifying and combining audio tracks for musical mashups due to structural differences and the scarcity of acapella and instrumental content, often resulting in cacophony.
Innovation Solution
A web-based recommendation tool that generates tags for audio recordings stored on third-party services, allowing users to select matching acapella and instrumental tracks based on parameters like key, tempo, and structure, with options for random selection and adjustments to ensure a harmonious mix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user manually searches for and selects two audio tracks to create a mashup, then the user can create a mashup, but it is difficult to identify tracks that fit well together due to structural differences and the scarcity of acapella and instrumental content
Solution Approach 1:
The system performs preliminary actions by pre-tagging audio tracks with structural information (key, tempo, time signatures, section lengths) before the user needs to create a mashup. This advance preparation enables quick matching and recommendation without requiring users to manually analyze track structures, thus improving ease of operation while maintaining high matching quality.
Solution Approach 2:
The patent introduces an intermediary recommendation system that acts as a mediator between the user and the vast library of audio tracks. This intermediary analyzes track compatibility based on multiple parameters and presents pre-filtered options, reducing the complexity of manual search while ensuring high-quality matches through systematic evaluation of structural compatibility.
2Quantity of substance
If the system stores actual audio recordings in the database, then the system can store and process the audio, but the database takes up a lot of space and can store a wider variety of musical material
Solution Approach 1:
The system extracts only the essential metadata and structural tags from audio recordings, storing this information in the database while leaving the actual audio files on third-party services. This extraction approach maintains the ability to analyze and match tracks based on key, tempo, structure, and other parameters without requiring substantial storage space, thus achieving high content variety with minimal database volume.
Solution Approach 2:
Instead of storing original audio recordings, the system creates and stores simplified copies in the form of structured tags and metadata. These tag copies contain all necessary information for matching and recommendation (key, tempo, time signatures, section lengths) while occupying minimal space, enabling the database to handle a much larger variety of musical content.
3Adaptability or versatility
If two tracks with significant structural differences are combined, then a mashup can be created, but the result is cacophony and sounds bad
Solution Approach 1:
The system systematically evaluates multiple parameters including key, tempo, time signatures, and section lengths to identify compatible tracks. By analyzing and matching these parameters, the system ensures that combined tracks have compatible structural characteristics, preventing cacophony while maintaining flexibility in track selection across different genres and styles.
Solution Approach 2:
The recommendation system incorporates feedback mechanisms that evaluate track compatibility based on structural parameters and provide guidance to users. The system analyzes the interaction between potential track combinations and provides feedback on expected compatibility, allowing users to make informed decisions that ensure high-quality mashup results while maintaining creative flexibility.
Data Source
AI summary
A music mashup recommendation system is presented, comprising a database of acapella (isolated vocals) and instrumental (no vocals) recordings, wherein the recordings themselves are stored on a third-party service such as YouTube or Spotify and only the links are stored in a database, along with tags that describe the musical composition in detail. The tags are then used to generate recommendations for potential mashups between acapella and instrumental tracks that have a high degree of similarity, so that even a musically untrained user can generate a mashup of good quality. One or both tracks could be selected randomly by the system based on the tags selected.