A Real-Time Feedback System for Evaluating Musical Works Using Audio Signal Processing and Machine Learning Models
Description: This invention relates to an innovative system for providing real-time feedback in the evaluation of musical works. By leveraging audio signal processing and machine learning models, it analyzes and assesses the quality and technical features of musical pieces in real time during performance or recording.
In this system, the audio signal from the musical piece is captured by a professional microphone and, after being digitized via a digital sound card, is transmitted to a processing computer. The software modules designed in this invention include: • Audio Pre-processing Module (for noise reduction and signal preparation) • Audio Feature Extraction Module (including rhythm, tempo, tonality, harmony of audio layers, clarity, intensity, harmony, and other standard indicators) • Machine Learning Module (using pre-trained models such as CNN and RNN to analyze audio patterns) • Real-time Feedback Module (providing numerical and descriptive reports instantly) • Data Storage and Record Management Unit These components work together seamlessly. A key feature of this system is its ability to deliver specialized, standardized feedback to music producers at the exact moment of performance or recording. This allows the artist or sound engineer to be immediately informed of the status of technical indicators and to make adjustments or improvements to weaker sections as needed. The objectives and advantages of this invention include: • Standardizing the quality assessment process for musical works • Reducing the time and cost of professional analysis • Eliminating human error and subjective bias in evaluations • Enhancing productivity and the final quality of produced works • The possibility of implementation as a desktop software, mobile application, and web-based system • The ability to analyze multiple audio parameters simultaneously This system is suitable for use in professional music studios, music education classes, independent producers, music competitions, and audio and music research centers. It can serve as a complementary analytical tool in the production and recording process of audio and musical works.
Organisation: IRTI
Innovator(s): Milad Mahanrad
Category: Information Technology, AI and ML
Country: Iran
