Real-time chord identification application: Enabling lifelong music education through seamless integration of audio processing and machine learning

Lifelong music education is critical need for all with a particular focus on adult learners and seniors. One of the difficulties in music education is identifying chords accurately. This is a preliminary study to develop a chord identification application using Artificial Intelligence (AI) technologies. I seek to answer the key research question of how audio processing algorithms and deep learning models can be used to provide real-time, accurate and user-friendly chord recognition that meets the diverse needs of adult learners and senior citizens. Our overall goal is to create an application that not only assists with chord identification, but also fosters a lifelong love of music and learning. My methodology is based on the principles of adult and senior education initiatives and includes the following key steps: using ready-made datasets for audio processing and feature extraction, transforming waveforms into mel spectrograms, and preparing and extending the datasets where necessary. I then train and optimise deep learning models, such as various convolutional neural network (CNN) architectures, to achieve high accuracy in chord recognition. By using advanced technologies and adhering to the principles of lifelong learning, our research aims to enhance the musical journey of individuals throughout their lives, contributing to both personal enrichment and cognitive well-being.