TY - GEN
T1 - Inferring social contexts from audio recordings using deep neural networks
AU - Asgari, Meysam
AU - Shafran, Izhak
AU - Bayestehtashk, Alireza
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/14
Y1 - 2014/11/14
N2 - In this paper, we investigate the problem of detecting social contexts from the audio recordings of everyday life such as in life-logs. Unlike the standard corpora of telephone speech or broadcast news, these recordings have a wide variety of background noise. By nature, in such applications, it is difficult to collect and label all the representative noise for learning models in a fully supervised manner. The amount of labeled data that can be expected is relatively small compared to the available recordings. This lends itself naturally to unsupervised feature extraction using sparse auto-encoders, followed by supervised learning of a classifier for social contexts. We investigate different strategies for training these models and report results on a real-world application.
AB - In this paper, we investigate the problem of detecting social contexts from the audio recordings of everyday life such as in life-logs. Unlike the standard corpora of telephone speech or broadcast news, these recordings have a wide variety of background noise. By nature, in such applications, it is difficult to collect and label all the representative noise for learning models in a fully supervised manner. The amount of labeled data that can be expected is relatively small compared to the available recordings. This lends itself naturally to unsupervised feature extraction using sparse auto-encoders, followed by supervised learning of a classifier for social contexts. We investigate different strategies for training these models and report results on a real-world application.
KW - Deep neural networks
KW - Harmonic model
KW - Multi-label classification
UR - http://www.scopus.com/inward/record.url?scp=84912544456&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84912544456&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2014.6958853
DO - 10.1109/MLSP.2014.6958853
M3 - Conference contribution
AN - SCOPUS:84912544456
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
A2 - Mboup, Mamadou
A2 - Adali, Tulay
A2 - Moreau, Eric
A2 - Larsen, Jan
PB - IEEE Computer Society
T2 - 2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014
Y2 - 21 September 2014 through 24 September 2014
ER -