N-gram Audio Playlist Generation with Backoff Probabilities
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Solution Overview
Problem
Conventional automated playlist generation mechanisms fail to create interesting and appealing playlists as they do not account for human factors such as musical themes, artist styles, and mood juxtapositions, resulting in less engaging playlists compared to those curated by humans.
Innovation Solution
A playlist generation mechanism using an N-gram statistical model based on human-generated playlists to identify statistically significant audio file ordering patterns, incorporating backoff probabilities to ensure diversity and leveraging class-based models for audio files with insufficient coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automated playlist generation mechanisms are used, then playlists can be generated automatically without manual effort, but the playlists lack human factors such as musical themes, artist styles, and mood juxtapositions making them less interesting and appealing
Solution Approach 1:
The patent introduces an N-gram statistical model as an intermediary between automated generation and human-like playlist quality. The model learns from human-generated playlists to capture patterns in song transitions, themes, and moods, then uses these patterns to guide automated playlist generation, bridging the gap between automation and human-like quality
Solution Approach 2:
The patent copies patterns from human-generated playlists by analyzing large datasets of existing playlists to extract statistical patterns in song ordering, transitions, and groupings. These copied patterns are then applied to generate new playlists that replicate the interesting characteristics of human-curated playlists
2Device complexity
If simple criteria such as acoustic similarity or random selection are used for automated playlist generation, then the generation process is computationally simple and fast, but the resulting playlists lack interesting juxtapositions of songs and the human element expected by listeners
Solution Approach 1:
The patent replaces simple mechanical selection criteria (random selection, acoustic similarity) with a statistical model-based system. The N-gram model uses probability distributions learned from data to select songs, substituting straightforward mechanical rules with a more sophisticated statistical approach that captures human-like playlist patterns
3Ease of manufacture
If automated playlist generators use conventional mechanisms like random selection or acoustic similarity, then the system remains simple to implement, but the playlists are less appealing and interesting compared to human-generated playlists
Solution Approach 1:
The patent performs preliminary action by pre-training the N-gram statistical model on large datasets of human-generated playlists before actual playlist generation. This pre-learning phase captures human preferences and patterns in advance, so that during actual use, the system can quickly generate high-quality playlists without requiring complex real-time analysis
Data Source
AI summary
Method and apparatus for programmatically generating interesting audio file playlists. A playlist generation mechanism may use an N-gram model of audio file ordering patterns found in a collection of human-generated playlists to automatically generate new playlists. Given play histories indicating one or more played audio files as input, statistical methods may be used to look for sequences of audio files that occur a statistically significant number of times in the N-gram model for inclusion in new, interesting playlists that incorporate the human element found in the collection of playlists. In some embodiments, one more backoff probability methods may be used to provide additional candidate audio files for playlists if there is insufficient coverage for an audio file in the N-gram model. In one embodiment, a class-based statistical model incorporating higher-level statistics for the audio files may be used to weight selection of audio file transitions from the N-gram model.