A Study on the Transferability of Adversarial Attacks in Sound Event Classification

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks

Audio tagging using a linear noise modelling layer

Label noise refers to the presence of inaccurate target labels in a dataset. It is an impediment to the performance of a deep neural network (DNN) as the network tends to overfit to the label noise, hence it becomes imperative to devise a generic methodology to counter the effects of label noise. FSDnoisy18k is an audio dataset collected with the aim of encouraging research on label noise for sound event classification. The dataset contains ~42.5 hours of audio recordings divided across 20 classes, with a small amount of manually verified labels and a large amount of noisy data. Using this dataset, our work intends to explore the potential of modelling the label noise distribution by adding a linear layer on top of a baseline network. The accuracy of the approach is compared to an alternative approach of adopting a noise robust loss function. Results show that modelling the noise distribution improves the accuracy of the baseline network in a similar capacity to the soft bootstrapping loss

Observed anomalous upwelling in the Lakshadweep Sea during the summer monsoon season of 2005

Repeat near-fortnightly expendable bathythermograph (XBT) transects made along Kochi-Kavaratti (KK) shipping lane in the Lakshadweep Sea (LS) during 2002–2006 are examined to describe the observed year-to-year variability of upwelling during summer monsoon season (SMS). Among all the years, the upwelling characterized by up-sloping of 25°C isotherm is relatively weaker and persisted until November during SMS of 2005 and is stronger during the SMS of 2002. As a result of prolonged upwelling, the sea surface temperature has shown cooling extending into the postmonsoon season. The estimated marine pelagic fish landings along the southwest coast of India (SWCI) have also shown increase until December. The governing mechanisms both in terms of local and remote forcings are examined to explain the observed anomalous upwelling during SMS of 2005. The equatorward alongshore wind stress (WS) along the KK XBT transect persisted in a transient manner beyond September only during SMS of 2005. The westerly wind bursts over the equator during the winter of 2004–2005 are both short-lived and relatively weaker triggering weaker upwelling Kelvin waves that propagated into LS in the following SMS of 2005. The observed distribution of negative sea surface height anomaly in the LS is relatively weaker during the SMS of 2005 and lasted longer. The correlation analysis suggests that the local alongshore WS off the SWCI and the remote forcing from the southern coast of Sri Lanka has greater influence on the observed interannual variability of upwelling in the LS when compared to the remote forcing from the equator

Temperature and depth error in the Mechanical Bathythermograph data from the Indian Ocean

1288-1291For arriving at realistic estimates of warming rate of oceans at regional and global level, it is important to understand persistent biases in observations of ocean temperature. In the present study, collocated MBT and CTD data from the Indian Ocean are used to understand the observed errors in temperature and depth of MBT data. The estimated error from the match up data shows that both temperature and depth measurement of MBT are over estimated, compared to CTD measurements. Estimated thermal bias is 0.14°C during the study period, which is significantly higher compared to earlier reports. The corrected MBT data for thermal bias and depth error shows a significant reduction in the existing error