Indian Musical Instrument Recognition Using Multi-Layered Neural Networks
Ishika Amit Khatri, Anushka Mankar, Jayashri Bagade, Suruchi Gaurav Dedgaonkar, Mrugank Shirurkar · 2025
Automatic recognition of musical instruments from audio signals plays a crucial role in various applications, including music production, transcription, and interactive music interfaces. The suggested method uses a multi-layer architecture with ReLU activation functions and makes use of features taken from audio recordings. It is possible to get encouraging results in differentiating between distinct instrument classes with rigorous training and evaluation. The technology may be used in music-related fields in the real world. Prospective avenues for investigation encompass investigating group techniques, managing cacophonous settings, and incorporating the model into software used for music creation. The proposed method presents a promising step toward automatic instrument recognition, with potential applications in music analysis, production, and real-time interactive systems. By extending the current research and addressing key challenges, we envision the development of more advanced systems capable of providing deeper insights into musical compositions and enhancing creative workflows.