International Research Journal of Education and Technology

The advent of artificial intelligence has opened new avenues in music synthesis, enabling the recreation of traditional musical instruments through computational models. This paper presents an approach to generating sounds resembling traditional musical instruments using Recurrent Neural Networks (RNNs). RNNs are particularly suited for sequential data, making them ideal for modelling audio waveforms and capturing the intricate temporal patterns inherent in musical sounds. Our model is trained on datasets comprising audio samples from various traditional instruments, emphasizing both tonal fidelity and temporal dynamics. By leveraging RNN architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), the system is designed to learn and replicate the acoustic properties unique to each instrument. The generated sounds are evaluated using objective metrics like spectrogram analysis and subjective listening tests to ensure their authenticity and quality. This research demonstrates the potential of RNN-based systems in bridging the gap between technology and tradition, offering tools for musicians, composers, and cultural preservationists. Furthermore, the methodology can serve as a foundation for advanced applications in music education, virtual instruments, and the preservation of endangered musical traditions.

Keywords – Music synthesis - Traditional musical instruments - Long Short-Term Memory (LSTM) - Gated Recurrent Units (GRU) - Audio waveform modeling - Temporal patterns - Spectrogram analysis - Acoustic fidelity

• Artificial intelligence in music - Virtual instruments - Cultural preservation - Endangered musical traditions