AiMi Inc. (20240290307). Machine Learning Model Trained based on Music and Decisions Generated by Expert System simplified abstract
Contents
Machine Learning Model Trained based on Music and Decisions Generated by Expert System
Organization Name
Inventor(s)
Edward Balassanian of Austin TX (US)
Patrick E. Hutchings of Melbourne (AU)
Machine Learning Model Trained based on Music and Decisions Generated by Expert System - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240290307 titled 'Machine Learning Model Trained based on Music and Decisions Generated by Expert System
Simplified Explanation: The patent application describes techniques for training a machine learning model to generate audio data similar to a music generator program. A computer system selects and combines musical expressions to create audio data, and then trains a machine learning model to do the same. The model receives information from the generator program to make decisions on expression selection and mixing to generate music compositions.
- The computer system uses a rules-based music generator program to select and combine musical expressions.
- The machine learning model is trained to select and combine musical expressions to generate music compositions.
- The model receives information from the generator program to make decisions on expression selection and mixing.
- The computer system compares the generator information to the decisions made by the machine learning model and updates the model accordingly.
Potential Applications: This technology could be used in music production, sound design, and audio synthesis applications. It could also be applied in the development of AI-powered music composition tools and interactive music generation systems.
Problems Solved: This technology addresses the challenge of creating realistic and diverse music compositions using machine learning. It streamlines the process of generating audio data by automating the selection and combination of musical expressions.
Benefits: The technology enables the creation of unique and high-quality music compositions. It enhances the efficiency of music production processes and provides a tool for musicians and composers to explore new creative possibilities.
Commercial Applications: "Machine Learning Model for Music Composition" could revolutionize the music industry by offering innovative tools for music production studios, composers, and artists. It could also be integrated into music streaming platforms to enhance user experience and offer personalized music recommendations.
Prior Art: Prior art related to this technology may include research on machine learning in music composition, audio synthesis techniques, and music generation algorithms. Researchers and developers in the fields of artificial intelligence and music technology may have explored similar concepts.
Frequently Updated Research: Researchers are constantly exploring advancements in machine learning algorithms for music composition and audio generation. Stay updated on recent studies and developments in the intersection of AI and music technology to understand the latest trends in this field.
Questions about Machine Learning Model for Music Composition: 1. How does this technology improve the efficiency of music composition processes? 2. What are the potential implications of integrating this technology into music streaming platforms?
Original Abstract Submitted
techniques are disclosed that pertain to training a machine learning model to generate audio data similar to a music generator program. a computer system, executing a rules-based music generator program, selects and combines multiple musical expressions to generate audio data. the computer system trains a machine learning model to select and combine musical expressions to generate music compositions. the machine learning model receives generator information by the generator program that indicates expression selection decisions to generate the audio data, mixing decisions to generate the audio data, and first audio information output based on the generator program's expression selection decisions and the mixing decisions. the computer system compares the generator information to expression selection decisions, mixing decisions, and second audio information generated by the machine learning model based on the machine learning model's expression selection decisions and mixing decisions. the computer system updates the machine learning model based on the comparing.
