AI Vocal Training System for Personalized Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online vocal training tools lack the ability to assess and provide personalized feedback tailored to individual users' voices and goals, failing to offer customized exercises that effectively improve vocal quality and range, as they do not account for human factors like comfortability and vocal health.
Innovation Solution
A computer-implemented system that measures and assesses various aspects of a user's voice, learns personalized attributes, and provides individualized feedback and exercises using a combination of user-reported data and artificial intelligence analysis, generating a vocal profile to tailor training specifically to the user's needs and goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated online vocal training tools are used, then accessibility and convenience are improved, but the ability to provide personalized feedback and assess vocal quality deteriorates
Solution Approach 1:
The patent replaces the mechanical system of human vocal coaches with an automated digital system that uses machine learning models and audio analysis algorithms to assess vocal quality, provide feedback, and generate training exercises. This substitution maintains accessibility while restoring measurement precision through consistent, data-driven evaluation.
Solution Approach 2:
The system enables users to conduct self-assessment of their vocal quality through automated analysis of their recorded vocals. The machine learning models evaluate pitch, timbre, and other vocal parameters, allowing users to receive personalized feedback without requiring external coaches, thus improving both accessibility and assessment capability.
2Reliability
If generic vocal training apps are used, then cost is reduced, but the ability to provide individualized feedback and customized exercises deteriorates
Solution Approach 1:
The system dynamically adapts training content based on continuous assessment of user performance and vocal characteristics. The machine learning models generate customized exercises that evolve as users improve, providing individualized feedback tailored to each user's specific needs, goals, and progress while maintaining cost-effectiveness through automation.
Solution Approach 2:
The system performs preliminary assessment of user vocal quality, experience level, and goals before generating personalized training programs. This preliminary analysis enables the system to customize exercises and feedback from the outset, matching the adaptability of human coaches while maintaining the cost benefits of automated delivery.
3Ease of operation
If current online tools are used, then accessibility is improved, but the ability to measure and assess vocal aspects such as tessitura, vocal quality, and stamina deteriorates
Solution Approach 1:
The system implements continuous feedback loops where user vocal performances are analyzed by machine learning models that assess multiple parameters including tessitura, vocal quality, and stamina. This feedback is used to generate personalized insights and adjust training programs, ensuring comprehensive vocal assessment is preserved in the automated system while maintaining accessibility.
Data Source
AI summary
A computer-implemented system and method for vocal training. A user's voice is measured and assessed. Personalized attributes about the user are also acquired including goals of the user. Based on measured aspects of a user's voice, and attributes acquired about the user (based on a combination of user-reported data, mechanically-assessed and/or artificial intelligence-determined analysis), (1) a report is generated about the user's vocal quality and ability, and (2) the user is given individualized feedback, lessons, and vocal exercises specific to the user's voice, vocal ability, voice-comfort-zone boundaries, and the user's goals in a scientific manner in the form of a virtual-vocal coach. The techniques and goals may be given to the user in real time, and/or used to generate new exercises and drills. By constantly measuring and scoring a user's progress, an ongoing-overall-voice strategy is generated to help the user meet the user's ongoing vocal-development goals.