AI Audio Ad Selection Matching Musicological Features
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Solution Overview
Problem
In personalized media distribution environments, inserting advertisements into audio content streams disrupts the user experience due to mismatched ad content, leading to reduced revenue for content servers as users terminate applications to avoid ads.
Innovation Solution
A computer-implemented method and system that uses artificial intelligence to select audio advertisements based on musicological characteristics, matching the similarity of candidate audio advertisements with the reference music item's features, ensuring coherent content presentation and improved user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If advertisements are inserted into audio content streams, then content server revenue is improved, but user experience is disrupted
Solution Approach 1:
The patent applies local quality by matching advertisements to specific musical characteristics (genre, tempo, mood, instrumentation) of the surrounding audio content. Each advertisement is selectively placed based on its compatibility with the local musical context, ensuring that ads blend harmoniously with the content rather than disrupting it uniformly across all placements.
Solution Approach 2:
The system changes parameters by analyzing multiple musical dimensions (genre, tempo, key, mood, instrumentation) to select advertisements. By varying the selection criteria based on the specific musical parameters of the surrounding content, the system dynamically adapts ad placement to maintain user experience while maximizing revenue opportunities.
2Stability of the object's composition
If manually selected and sequenced ads are used, then ad coherency is improved, but device complexity and operational feasibility worsen in personalized media environments
Solution Approach 1:
The system implements self-service by automatically analyzing the musical characteristics of audio content and autonomously selecting and sequencing advertisements based on compatibility algorithms. The system serves itself by making intelligent ad placement decisions without requiring manual intervention, thereby maintaining coherency while scaling to personalized media environments.
Solution Approach 2:
The patent replaces the mechanical system of manual ad selection with an automated computational system that uses music analysis algorithms and similarity metrics. This substitution eliminates the need for human operators while maintaining or improving ad coherency through consistent, data-driven decision-making across thousands of personalized channels.
3Productivity
If ads are inserted without matching musicological characteristics, then content server productivity is improved, but user engagement and revenue worsen due to terminations
Solution Approach 1:
The system performs preliminary action by pre-analyzing the musical characteristics of both the audio content and candidate advertisements before making placement decisions. By evaluating genre, tempo, mood, and other musicological features in advance, the system prepares compatibility assessments that enable rapid, accurate ad selection without compromising user engagement or requiring post-placement adjustments.
Data Source
AI summary
A content server uses a form of artificial intelligence such as machine learning to identify audio content with musicological characteristics. The content server obtains an indication of a music item presented by a client device and obtains reference music features describing musicological characteristics of the music item. The content server identifies candidate audio content associated with candidate music features. The candidate music features are determined by analyzing acoustic features of the candidate audio content and mapping the acoustic features to music features according to a music feature model. Acoustic features quantify low-level properties of the candidate audio content. One of the candidate audio content items is selected according to comparisons between the candidate music features of the candidate audio advertisements and the reference music features of the music item. The selected audio content is provided the client device for presentation.