TY - GEN
T1 - SEMI-SUPERVISED GAUSSIAN MIXTURE VARIATIONAL AUTOENCODER FOR PULSE SHAPE DISCRIMINATION
AU - Abdulaziz, Abdullah
AU - Zhou, Jianxin
AU - Di Fulvio, Angela
AU - Altmann, Yoann
AU - McLaughlin, Stephen
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - We address the problem of pulse shape discrimination (PSD) for radiation sources characterization by leveraging a Gaussian mixture variational autoencoder (GMVAE). When using PSD to characterize radiation sources, the number of emission sources and types of pulses to be classified is usually known. Yet, the creation of labeled data can be challenging for some classes as it requires expensive expert annotation. In this context, GMVAE can learn the distinct features of pulses from only unlabeled data. We show that classification accuracy can be further enhanced by adopting a semi-supervised GMVAE with auxiliary loss functions when labeled data are available. The preliminary results on two datasets with different number of classes suggest superior performance of GMVAE compared to other classifiers such as Gaussian mixture model (GMM) for unsupervised and semi-supervised learning and random forest for supervised learning.
AB - We address the problem of pulse shape discrimination (PSD) for radiation sources characterization by leveraging a Gaussian mixture variational autoencoder (GMVAE). When using PSD to characterize radiation sources, the number of emission sources and types of pulses to be classified is usually known. Yet, the creation of labeled data can be challenging for some classes as it requires expensive expert annotation. In this context, GMVAE can learn the distinct features of pulses from only unlabeled data. We show that classification accuracy can be further enhanced by adopting a semi-supervised GMVAE with auxiliary loss functions when labeled data are available. The preliminary results on two datasets with different number of classes suggest superior performance of GMVAE compared to other classifiers such as Gaussian mixture model (GMM) for unsupervised and semi-supervised learning and random forest for supervised learning.
KW - Gaussian mixture variational autoencoder
KW - Semi-supervised classification
KW - pulse shape discrimination
UR - http://www.scopus.com/inward/record.url?scp=85131231655&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131231655&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9747313
DO - 10.1109/ICASSP43922.2022.9747313
M3 - Conference contribution
AN - SCOPUS:85131231655
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3538
EP - 3542
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -