AI Melody Generation via Emotion and Environment Sensing
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Solution Overview
Problem
Conventional music playback systems fail to automatically adjust music styles according to user emotions and environmental conditions, requiring manual user intervention to match music with the current situation.
Innovation Solution
A computer-implemented solution that automatically generates a melody by selecting a melody feature parameter based on user emotion and environmental information, using a Variational Autoencoder (VAE) model to create a melody that conforms to a specific music style, differing from reference melodies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional music playback systems are used, then music can be played from a predefined list, but the system cannot automatically adjust music styles according to user emotions and environmental conditions
Solution Approach 1:
The system automatically detects user emotion through audio analysis and environmental conditions through sensors, then autonomously selects and generates appropriate music styles without requiring manual user intervention. The melody generation model self-adjusts parameters based on detected inputs, enabling the system to serve itself in adapting music to context.
Solution Approach 2:
The system changes musical parameters (tempo, pitch, rhythm, instrumentation) dynamically based on detected user emotion and environmental conditions. The melody generation model adjusts these parameters in real-time to match the detected context, transforming the music output to adaptability requirements.
2Ease of operation
If manual user operations are required to switch songs, then user control is maintained, but the convenience and ease of operation is reduced
Solution Approach 1:
The system performs automatic song selection and switching based on detected user emotion and environmental context, eliminating the need for manual user operations. The melody generation model autonomously creates and transitions between music pieces, making the system self-sufficient in managing music playback without user intervention.
3Adaptability or versatility
If a fixed music list is used, then music playback is simple, but the system lacks ability to dynamically match music with current situation
Solution Approach 1:
The patent replaces traditional mechanical music selection methods (predefined lists, manual switching) with an AI-based melody generation model. This neural network model automatically composes music by learning from reference melodies and adjusting to detected user emotion and environmental conditions, substituting complex adaptive behavior for simple fixed playlists.
Solution Approach 2:
The system dynamically changes multiple musical parameters simultaneously (tempo, pitch contour, rhythm patterns, instrumentation selection) based on detected context. The melody generation model adjusts these parameters in coordination to create music that adapts to user emotion and environment, achieving dynamic adaptation through parameter transformation.
Data Source
AI summary
Implementations of the subject matter described herein provide a solution that enables a machine to automatically generate a melody. In this solution, user emotion and/or environment information is used to select a first melody feature parameter from a plurality of melody feature parameters, wherein each of the plurality of melody feature parameters corresponds to a music style of one of a plurality of reference melodies. The first melody feature parameter is further used to generate a first melody that conforms to the music style and is different from the reference melody. Thus, a melody that matches user emotions and/or environmental information may be automatically created.