Abstract: Sound classification is a multifaceted task that necessitates the gathering and processing of vast quantities of data, as well as the construction of machine learning models that can accurately distinguish between various sounds. In our project, we implemented a novel methodology for classifying both musical instruments and environmental sounds, utilizing convolutional and recurrent neural networks. We used the Mel Frequency Cepstral Coefficient (MFCC) method to extract features from audio, which emulates the human auditory system and produces highly distinct features. Knowing how important data processing is, we implemented distinctive approaches, including a range of data augmentation and cleaning techniques, to achieve an optimized solution. The outcomes were noteworthy, as both the convolutional and recurrent neural network models achieved a commendable level of accuracy. As machine learning and deep learning continue to revolutionize image classification, it is high time to explore the development of adaptable models for audio classification. Despite the challenges associated with a small dataset, we successfully crafted our models using convolutional and recurrent neural networks. Overall, our strategy for sound classification bears significant implications for diverse domains, encompassing speech recognition, music production, and healthcare. We hold the belief that with further research and progress, our work can pave the way for breakthroughs in audio data classification and analysis.

Karim Mohammed Rezaul, Md. Jewel, Md Shabiul Islam, Kazy Noor e Alam Siddiquee, Nick Barua, Muhammad Azizur Rahman, Mohammad Shan-A-Khuda, Rejwan Bin Sulaiman, Md Sadeque Imam Shaikh, Md Abrar Hamim, F.M Tanmoy, Afraz Ul Haque, Musarrat Saberin Nipun, Navid Dorudian, Amer Kareem, Ahmmed Khondokar Farid, Asma Mubarak, Tajnuva Jannat and Umme Fatema Tuj Asha. “Enhancing Audio Classification Through MFCC Feature Extraction and Data Augmentation with CNN and RNN Models”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150704

@article{Rezaul2024,
title = {Enhancing Audio Classification Through MFCC Feature Extraction and Data Augmentation with CNN and RNN Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150704},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150704},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Karim Mohammed Rezaul and Md. Jewel and Md Shabiul Islam and Kazy Noor e Alam Siddiquee and Nick Barua and Muhammad Azizur Rahman and Mohammad Shan-A-Khuda and Rejwan Bin Sulaiman and Md Sadeque Imam Shaikh and Md Abrar Hamim and F.M Tanmoy and Afraz Ul Haque and Musarrat Saberin Nipun and Navid Dorudian and Amer Kareem and Ahmmed Khondokar Farid and Asma Mubarak and Tajnuva Jannat and Umme Fatema Tuj Asha}
}