Adaptive Playlisting With Joint Audio-Text Mood Embeddings
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Solution Overview
Problem
Existing music playlist generation systems fail to adapt in real-time to the user's mood during a listening session, lacking the ability to incorporate user behavior and natural language inputs effectively.
Innovation Solution
A two-tower model is employed to generate a joint audio-text embedding space, using an audio embedding network and a text embedding network to analyze user preferences, allowing for the generation of an updated playlist that adapts to the user's mood during a listening session by incorporating user behavior and natural language inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If music playlists are generated based on user listening history and seed songs at the start of each listening session, then the playlist generation is efficient and quick, but the playlist cannot adapt in real-time to the user's mood during the listening session
Solution Approach 1:
The patent implements dynamic playlist adaptation by continuously monitoring user behavior (listen/skip patterns) and natural language inputs during the listening session. The system updates the playlist in real-time based on inferred user mood, transforming the static pre-generated playlist into a dynamic, adaptive experience that evolves throughout the session.
Solution Approach 2:
The system incorporates feedback loops by analyzing user listen/skip behavior and natural language inputs to infer mood changes. This feedback is processed through the two-tower model to generate updated audio embeddings, which then trigger playlist re-generation, creating a continuous feedback cycle that adapts the playlist to user preferences throughout the session.
2Measurement precision
If the system incorporates user behavior analysis and natural language processing to infer user mood, then the playlist personalization accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex processing task into two separate towers: an audio embedding network that processes music tracks and a text embedding network that processes natural language inputs. This segmentation allows each tower to specialize in its respective modality, improving accuracy while making the overall system more manageable and trainability.
Solution Approach 2:
The patent introduces a joint audio-text embedding space as an intermediary representation that bridges audio and text modalities. This shared embedding space allows the system to process both user behavior data and natural language inputs through a unified framework, simplifying the integration of multiple data sources while maintaining high inference accuracy.
3Measurement precision
If the system uses a two-tower model with audio and text embedding networks to generate joint embeddings, then the semantic similarity measurement is more accurate, but the computational resources required increase
Solution Approach 1:
The patent pre-trains the two-tower model on large datasets of audio-text pairs to establish the joint embedding space before deployment. This preliminary training allows the model to learn robust semantic relationships in advance, so that during actual playlist generation, the computational burden is reduced to calculating embeddings for current user inputs rather than training from scratch each time.
Solution Approach 2:
The patent employs parameter sharing and dimensionality reduction in the embedding networks to balance accuracy and computational efficiency. By optimizing the embedding dimension and sharing parameters between towers where appropriate, the system achieves high semantic similarity measurement accuracy while controlling the computational resources required for real-time playlist adaptation.
Data Source
AI summary
A method includes providing, by an audio playback interface, an initial playlist comprising audio tracks. The method includes receiving a user preference associated with an initial audio track during a listening session, wherein the user preference is indicative of a listening mood of a user and comprises one or more of a user behavior or a natural language input. The method includes generating a representation of the user preference in a joint audio-text embedding space by applying a two-tower model comprising an audio embedding network and a text embedding network. A proximity of two embeddings is indicative of semantic similarity. The method includes training a machine learning model to generate an updated playlist responsive to the listening mood of the user during the listening session. The method includes applying the machine learning model to generate the updated playlist. The method includes substituting the initial playlist with the updated playlist.