Personalized Song Generation via Nested Language Models
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Solution Overview
Problem
Current AI chatbots lack the ability to provide personalized song experiences for users, failing to integrate user language styles and emotional contexts effectively into song generation and assessment.
Innovation Solution
A chatbot system that utilizes a personal language model, a public song language model, and a topic-emotion graph to generate personalized lyrics and songs, allowing users to input messages or keywords to create customized songs, and assess singing performances by comparing spectrograms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional AI chatbot uses keyword matching and natural language processing to respond to users, then the system is simple and easy to operate, but it cannot provide personalized song experiences or integrate user language styles and emotional contexts effectively
Solution Approach 1:
The patent implements nested language models where a personal language model is built upon and integrated with a public song language model. The personal language model captures user-specific language styles and preferences, while the public song language model provides general song generation capabilities. This nested structure enables personalized song generation without requiring a completely new system, thus improving adaptability while managing complexity through layered integration
Solution Approach 2:
The system performs preliminary action by pre-training language models on extensive datasets (public song lyrics and user-specific communication data) before actual song generation. The personal language model is trained in advance on user communication records to capture individual language styles, and the public song language model is pre-trained on song lyric databases. This preliminary training enables the system to quickly generate personalized songs without complex real-time processing during user interaction
2Adaptability or versatility
If the chatbot generates personalized songs based on user messages, then user engagement and personalization are improved, but the time required to generate and process songs increases
Solution Approach 1:
The system performs preliminary action by pre-training language models on extensive datasets (public song lyrics and user-specific communication data) before actual song generation. The personal language model is trained in advance on user communication records to capture individual language styles, and the public song language model is pre-trained on song lyric databases. This preliminary training enables the system to quickly generate personalized songs without complex real-time processing during user interaction
Solution Approach 2:
The system uses copying by generating song lyrics that mimic and replicate the user's language style captured in the personal language model. Instead of creating entirely new content from scratch, the system copies the user's linguistic patterns, vocabulary, and stylistic features, then applies them to song template structures. This copying approach enables rapid personalization while maintaining consistency with user preferences
3Manufacturing precision
If the system integrates personal language models and public song language models to generate personalized lyrics, then the quality and personalization of songs are improved, but the computational resources and processing complexity increase
Solution Approach 1:
The patent implements nested language models where a personal language model is built upon and integrated with a public song language model. The personal language model captures user-specific language styles and preferences, while the public song language model provides general song generation capabilities. This nested structure enables personalized song generation without requiring a completely new system, thus improving adaptability while managing complexity through layered integration
Solution Approach 2:
The system segments the song generation task into distinct components handled by different language models. The public song language model handles general lyric structure, rhyme schemes, and musicality, while the personal language model handles user-specific language patterns and personalization. This segmentation allows each model to specialize in specific aspects, improving overall lyric quality while managing computational complexity through divided responsibilities
4Measurement precision
If the chatbot provides comprehensive singing assessment through spectrogram analysis, then feedback accuracy is improved, but the measurement and detection difficulty increases
Solution Approach 1:
The system uses spectrogram analysis as an intermediary tool to bridge the gap between raw audio input and meaningful singing assessment. The spectrogram transforms complex audio signals into visual representations showing frequency, intensity, and time relationships. This intermediary representation makes it easier to objectively assess singing quality by providing clear metrics for pitch accuracy, timbre consistency, and other vocal characteristics without requiring complex direct audio analysis
Data Source
AI summary
The present disclosure provides method and apparatus for providing personalized songs in automated chatting. A message may be received in a chat flow. Personalized lyrics of a user may be generated based at least on a personal language model of the user in response to the message. A personalized song may be generated based on the personalized lyrics. The personalized song may be provided in the chat flow.