Comparing the Accuracy of CNN Model with Inception V3 for Music Instrument Recognition
Keywords:
Music Instrument recognition, Mel Frequency Cepstral Coefficients, Machine Learning, Deep LearningAbstract
Identification of music instruments from an audio signal is a complex but useful task in music information retrieval. Deep Learning and traditional machine learning models are extremely very useful in many music related tasks such as music genre classification, recognizing music similarity, identifying the singer etc. Music Instrument recognition and classification would be helpful in categorizing different categories of music. Many researchers have proposed models for classifying western music instruments. But very little research has been done in identifying instruments accompanied with South Indian music. This research aims at identifying string instrument such as violin and woodwind instrument such as flute accompanied in a Carnatic music concert and also in other categories of music. In order to identify the instruments accompanied, Convolutional Neural Network model and Inception V3 models were used. The Mel Frequency Cepstral Coefficients images were extracted from the audio input and fed in to the neural network model. The model has been trained for the above mentioned instruments, tested and validated on different types of audio input. This research also evaluates the performance of Inception V3 transfer learning model with CNN model in recognizing the instruments used in different categories of music.
Downloads
References
K. Sreekar, A. Devansh Reddy, “Musical Tones Classification using Machine Learning”, International Journal for Research in Applied Science & Engineering Technology, ISSN: 2321-9653. Volume 10, Issue XII, December 2022.
Christine Dewi , Rung-Ching Chen, “Deep Learning for Advanced Similar Musical Instrument Detection and Recognition”, International Journal of Computer Science, Volume 49, Issue 3, September 2022.
Saranga Kingkor Mahanta , Abdullah Faiz Ur Rahman Khilji , Partha Pakray, “Deep Neural Network for Musical Instrument Recognition using MFCCs”, Research Gate, http://dx.doi.org/10.13053/CyS-25-2-3946
Sujith. S, Jithin Kumar I J, Appu.V. “Musical Instrument Recognition System with Dynamic Feature Selection Method”, International Journal of Engineering Research & Technology. ISSN: 2278-0181. Volume 10, Issue 05, May-2021.
P. Aurchana, S. Prabavathy, “Musical Instruments Sound Classification using GMM”, London Journal of Social Sciences. Volume 1, 2021.
P. Kathirvel, R. Thiruvengatanadhan, R. Seenu, “Music Instrument Sound Classification using KNN”, International Journal of Creative Research Thoughts,ISSN: 2320-2882. Volume 9, Issue 12, December 2021.
Romulo Vieira , Joao Teixeira Ara ujo , Edimilson Batista , Flavio Schiavoni , “Automatic classification of instruments from supervised methods of machine learning”, SBC-OpenLib, October 2021.
Sangeetha Rajesh,N J Nalini, “Musical Instrument Emotion Recognition Using Deep Recurrent Neural Network”, Elsevier, Volume 167,2020.
S. Prabavathy, V. Rathikarani, P. Dhanalakshmi, “Classification of Musical Instruments Using SVM and KNN”, International Journal of Innovative Technology and Exploring Engineering., ISSN:2278-3075. Volume 9, Issue 7, May 2020.
Alexandre M. Lucena, Caroline P. A. Moraes, Kenji Nose-Filho , Denis G. Fantinato, “Musical Instruments Recognition using Machine Learning Techniques MLP and SVM”, Brazilian Technology Symposium, October 2020.
Siddharth Gururani, Cameron Summers, Alexander Lerch,. “Instrument Activity Detection in Polyphonic Music Using Deep Neural Networks”, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), At: Paris, France. April 2019.
Arun Solanki, Sachin Pandey, “Music Instrument Recognition Using Deep Convolutional Neural Networks”, International Journal of Information Technology, Springer. January 2019.
Orchisama Das, “Musical Instrument Identification with Supervised Learning”, Semantic Scholar, Corpus ID: 203603297. 2019.
S. Prabavathy, V. Rathikarani, P. Dhanalakshmi, “An Enhanced Musical Instrument Classification using Deep Convolutional Neural Network”, International Journal of Recent Technology and Engineering, ISSN:2277-3878.Volume 8,Issue 4,November 2019.
Romain Hennequin, Anis Khlif, Felix Voituret, Manuel Moussallam, “Spleeter: A Fast and State-Of-The Art Music Source Separation Tool with Pre-Trained Models”, ISMIR 2019.
A.Muthumari, “Classification Analysis for Musical Instrument Signal”, International Conference for Phoenixes on Emerging Current Trends in Engineering and Management, 2018.
Shruti Chakraborty, Ranjan Parekh, “Improved Musical Instrument Classification Using Cepstral Coefficients and Neural Networks”, Semantic Scholar. DOI:10.1007/978-981-13-2345-4_10 Corpus ID: 70343606, 2018.
Long Nguyen,Dongyun Lin, Zhiping Lin, “Deep CNNs for Microscopic Iimage Classification by Exploiting Transfer learning and Feature Concatenation”, IEEE International Symposium on Circuits and Systems (ISCAS), May 2018
Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, Bryan Seybold, Malcolm Slaney, Ron J. Weiss, Kevin Wilson, “CNN Architectures For Large-Scale Audio Classification”, Semantic Scholar, DOI:10.1109/ ICASSP.2017.7952132 , Corpus ID: 8810481. January 2017.
Sumit S. Bhojane, Omkar G. Labhshetwar, Kshitiz Anand, Prof. S. R. Gulhane, “Musical Instrument Recognition Using Machine Learning Technique”, International Research Journal of Engineering and Technology, Volume 4, Issue 5, May 2017.
Taejin Park , Taejin Lee, “Musical Instrument Sound Classification With Deep Convolutional Neural Network Using Feature Fusion Approach”,. arXiv:1512.07370, December 2015.
Abhilasha, Preety Goswami, Prof. Makarand Velankar. “Study paper for Timbre identification in Sound”, International Journal of Engineering Research & Technology, ISSN: 2278-0181. Volume 2 Issue 10, October – 2013
Dr. D.S. Bormane, Ms. Meenakshi Dusane. “A Novel Techniques for Classification of Musical Instruments”, Information and Knowledge Management. ISSN 2224-5758. Volume 3, No.10, 2013.
Stephen McAdams, “Musical Timbre Perception The Psychology of Music”, Elsevier, December 2013.
Glenn Eric Hall , Hassan Ezzaidi , Mohammed Bahoura, “Study of Feature Categories for Musical Instrument Recognition”, Communications in Computer and Information Science Book Series. Volume 322,2012.
https://machinelearningmastery.com/how-to-use-transfer-learning-when-developing-convolutional-neural- network-models/
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
