Abstract: Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions.

Nilesh B. Korade, Mahendra B. Salunke, Amol A. Bhosle, Sunil M. Sangve, Dhanashri M. Joshi, Gayatri G. Asalkar, Sujata R. Kadu and Jayesh M. Sarwade, “Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160475

@article{Korade2025,
title = {Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160475},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160475},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Nilesh B. Korade and Mahendra B. Salunke and Amol A. Bhosle and Sunil M. Sangve and Dhanashri M. Joshi and Gayatri G. Asalkar and Sujata R. Kadu and Jayesh M. Sarwade}
}