Abstract
“The beautiful thing about music is that it is similar to a time machine. Only one song is needed to take a person back to a moment in time and nothing else. Music is an adhesive that holds people together.” The objective is to find a better machine learning (ML) algorithm which can predict the genre of songs. MIR is one of the greatest techniques used nowadays to get useful information from the music (audio signal). In this, we use CNN and RNN to classify the music clips. In this, we use CNN and RNN to classify the music clips. These are one of the deep learning architectures which has been immensely utilized for pattern recognition in the past. The neutral central network has a large scope to capture the informative characteristics of the music model variable with minimal prior knowledge provided. We will compare the performance of all models and record their outcomes in terms of predictive accuracy.
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References
Bahuleyan H (2018) Music genre classification using machine learning techniques, Sound (cs.SD); Audio and speech processing (eess.AS)
Reed J, Lee C-H (2006) A study on music genre classification based on universal acoustic models. ISMIR
Chang KK, Jang J-SR, Iliopoulos CS (2013) On music genre classification via compressive sampling. 2013 IEEE international conference on multimedia and Expo (ICME)
Panagakis Y, Kotropoulos C, Kotropoulos C (2009) Music genre classification using locality preserving non-negative tensor factorization and sparse representations. ISMIR
Music Genre Classification. https://www.kaggle.com/c/music-genre-classification/datasets
Bahuleyan H (2018) Music genre classification using machine learning techniques. arXiv.org
Lachmish M (2018) Music genre classification. https://medium.com/@matanlachmish/music-genre-classification-470aaac9833d
Saha S (2018) A comprehensive guide to convolutional neural networks. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
Shewalkar A, Nyavanandi D, Ludwig SA (2019) Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM AND GRU. JAISCR 9(4):235–245
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Gupta, R., Ashish, S., Shekhar, H., Dominic, M.D.S. (2022). Music Genre Classification Using CNN and RNN-LSTM. In: Sharma, D.K., Peng, SL., Sharma, R., Zaitsev, D.A. (eds) Micro-Electronics and Telecommunication Engineering . ICMETE 2021. Lecture Notes in Networks and Systems, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-16-8721-1_67
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DOI: https://doi.org/10.1007/978-981-16-8721-1_67
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