TY - GEN
T1 - Classification of Music Genres based on Mel Frequency Cepstrum Coefficients using Deep Learning Models
AU - Preetham, Manoj
AU - Panga, Jemimah Beulah
AU - Onesimu, J. Andrew
AU - Raimond, Kumudha
AU - Dang, Helen
N1 - Genre classification is indeed a vital task today since the number of songs produced on a regular basis keeps increasing. On average, around, 60,000 tracks are being uploaded per day on Spotify. So, classifying these tracks by genre is definitely an important...
Preetham, M., Panga, J.B., Andrew, J., Raimond, K., Dang, H. (2022). Classification of Music Genres Based on Mel-Frequency Cepstrum Coefficients Using Deep Learning Models. In: Peter, J.D., Fernandes, S.L., Alavi, A.H. (eds) Disruptive Technologies for Big Data and Cloud Applications. Lecture Notes in Electrical Engineering, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-19-2177-3_83
PY - 2022/8/2
Y1 - 2022/8/2
N2 - Genre classification is indeed a vital task today since the number of songs produced on a regular basis keeps increasing. On average, around, 60,000 tracks are being uploaded per day on Spotify. So, classifying these tracks by genre is definitely an important task for every musical streaming services and platforms. Due to the high classification performance of neural network models such as convolutional neural network (CNN), multi-layer perceptron (MLP), and long short-term memory network (LSTM) are used in this work to automatically classify music into to its genres based on Mel-frequency cepstrum coefficients (MFCCs) instead of manually entering the genre. We experimented the models with the GTZAN dataset and provided a comparative analysis on the classification efficiency of deep learning models. We achieved a classification of 70.42% for our proposed CNN model which is greater than the human accuracy and over other deep learning models.
AB - Genre classification is indeed a vital task today since the number of songs produced on a regular basis keeps increasing. On average, around, 60,000 tracks are being uploaded per day on Spotify. So, classifying these tracks by genre is definitely an important task for every musical streaming services and platforms. Due to the high classification performance of neural network models such as convolutional neural network (CNN), multi-layer perceptron (MLP), and long short-term memory network (LSTM) are used in this work to automatically classify music into to its genres based on Mel-frequency cepstrum coefficients (MFCCs) instead of manually entering the genre. We experimented the models with the GTZAN dataset and provided a comparative analysis on the classification efficiency of deep learning models. We achieved a classification of 70.42% for our proposed CNN model which is greater than the human accuracy and over other deep learning models.
KW - multilayer perceptron
KW - convulutional neural networks
KW - long short-term memory
KW - music genre classification
UR - https://www.scopus.com/pages/publications/85135836210
UR - https://www.scopus.com/pages/publications/85135836210#tab=citedBy
U2 - 10.1007/978-981-19-2177-3_83
DO - 10.1007/978-981-19-2177-3_83
M3 - Conference contribution
SN - 9789811921766
T3 - Lecture Notes in Electrical Engineering
SP - 891
EP - 907
BT - Disruptive Technologies for Big Data and Cloud Applications
A2 - Peter, J. Dinesh
A2 - Fernandes, Steven Lawrence
A2 - Alavi, Amir H.
PB - Springer
T2 - International Conference on Big Data and Cloud Computing, ICBDCC 2021
Y2 - 20 August 2021 through 21 August 2021
ER -