TY - GEN
T1 - Audio songs classification based on music patterns
AU - Sharma, Rahul
AU - Srinivasa Murthy, Y. V.
AU - Koolagudi, Shashidhar G.
N1 - Publisher Copyright:
© Springer India 2016.
PY - 2016
Y1 - 2016
N2 - In this work, effort has been made to classify audio songs based on their music pattern which helps us to retrieve the music clips based on listener’s taste. This task is helpful in indexing and accessing the music clip based on listener’s state. Seven main categories are considered for this work such as devotional, energetic, folk, happy, pleasant, sad and, sleepy. Forty music clips of each category for training phase and fifteen clips of each category for testing phase are considered; vibrato-related features such as jitter and shimmer along with the mel-frequency cepstral coefficients (MFCCs); statistical values of pitch such as min, max, mean, and standard deviation are computed and added to the MFCCs, jitter, and shimmer which results in a 19-dimensional feature vector. feedforward backpropagation neural network (BPNN) is used as a classifier due to its efficiency in mapping the nonlinear relations. The accuracy of 82% is achieved on an average for 105 testing clips.
AB - In this work, effort has been made to classify audio songs based on their music pattern which helps us to retrieve the music clips based on listener’s taste. This task is helpful in indexing and accessing the music clip based on listener’s state. Seven main categories are considered for this work such as devotional, energetic, folk, happy, pleasant, sad and, sleepy. Forty music clips of each category for training phase and fifteen clips of each category for testing phase are considered; vibrato-related features such as jitter and shimmer along with the mel-frequency cepstral coefficients (MFCCs); statistical values of pitch such as min, max, mean, and standard deviation are computed and added to the MFCCs, jitter, and shimmer which results in a 19-dimensional feature vector. feedforward backpropagation neural network (BPNN) is used as a classifier due to its efficiency in mapping the nonlinear relations. The accuracy of 82% is achieved on an average for 105 testing clips.
UR - https://www.scopus.com/pages/publications/84983200534
UR - https://www.scopus.com/inward/citedby.url?scp=84983200534&partnerID=8YFLogxK
U2 - 10.1007/978-81-322-2526-3_17
DO - 10.1007/978-81-322-2526-3_17
M3 - Conference contribution
AN - SCOPUS:84983200534
SN - 9788132225256
T3 - Advances in Intelligent Systems and Computing
SP - 157
EP - 166
BT - Proceedings of the 2nd International Conference on Computer and Communication Technologies, IC3T 2015
A2 - Bhateja, Vikrant
A2 - Mandal, Jyotsna Kumar
A2 - Raju, K. Srujan
A2 - Satapathy, Suresh Chandra
PB - Springer Verlag
T2 - 2nd International Conference on Computer and Communication Technologies, IC3T 2015
Y2 - 24 July 2015 through 26 July 2015
ER -