TY - GEN
T1 - Artificial Intelligence Methods for Automatic Music Transcription using Isolated Notes in Real-Time
AU - Oropeza, Jose Luis Oropeza
AU - Guerra, Sergio Suarez
AU - Lopez, Omar Velazquez
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - We introduce a comparative study of several features obtained from audio signal and methods of Artificial Intelligence employed for Automatic Music Transcription in real-time, specially using monophonic notes. Mel-frequency Cepstrum Coefficients (MFCC), Linear Prediction Coefficients (LPC) and Cochlear Mechanics Cepstrum Coefficient (CMCC) were the features used which are a set of coefficients obtained from our laboratory experiments, which in this paper demonstrated to be more effective for Automatic Music Transcription (ATM) than other characteristics such as Mel Frequency Cepstral Coefficients (MFCC). At same time, Vector Quantization (VQ), Hidden Markov Models (HMM), Gaussian Mixtures Models (GMM) and Artificial Neural Networks (ANN) for pattern classification task were used. The database consisted of 840 music notes, we analyzed 5 scales and 14 samples by musical note. The results obtained showed that Vector Quatization, HMM using CMCC_L&B_RA and GMM were the best methods of Artificial Inteligent for this task, while MFCC and CMCC_L&B_RA were the best features employed.
AB - We introduce a comparative study of several features obtained from audio signal and methods of Artificial Intelligence employed for Automatic Music Transcription in real-time, specially using monophonic notes. Mel-frequency Cepstrum Coefficients (MFCC), Linear Prediction Coefficients (LPC) and Cochlear Mechanics Cepstrum Coefficient (CMCC) were the features used which are a set of coefficients obtained from our laboratory experiments, which in this paper demonstrated to be more effective for Automatic Music Transcription (ATM) than other characteristics such as Mel Frequency Cepstral Coefficients (MFCC). At same time, Vector Quantization (VQ), Hidden Markov Models (HMM), Gaussian Mixtures Models (GMM) and Artificial Neural Networks (ANN) for pattern classification task were used. The database consisted of 840 music notes, we analyzed 5 scales and 14 samples by musical note. The results obtained showed that Vector Quatization, HMM using CMCC_L&B_RA and GMM were the best methods of Artificial Inteligent for this task, while MFCC and CMCC_L&B_RA were the best features employed.
KW - Artificial Neural Networks (ANN)
KW - Gaussian Mixture Models (GMM)
KW - Hidden Markov Models (HMM)
KW - Mel Frequency Cepstrum Coefficients (MFCC)
KW - Vector Quantization (VQ)
UR - http://www.scopus.com/inward/record.url?scp=85092050306&partnerID=8YFLogxK
U2 - 10.1109/MICAI46078.2018.00010
DO - 10.1109/MICAI46078.2018.00010
M3 - Contribución a la conferencia
AN - SCOPUS:85092050306
T3 - Proceedings of the Special Session - 2018 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
SP - 13
EP - 19
BT - Proceedings of the Special Session - 2018 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
A2 - Batyrshin, Ildar
A2 - de Lourdes Martinez Villasenor, Maria
A2 - Espinosa, Hiram Eredin Ponce
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
Y2 - 22 October 2018 through 27 October 2018
ER -