Recognizing Bird Species in Audio Files Using Transfer Learning

When choosing the number of units per layer, the learning rate and the number of training iterations to optimize classification error on the validation set, unsupervised

Figure 1: Effect of depth on performance for a model trained (top) without pre-training and (bottom) with pre- training, for 1 to 5 hidden layers (we were unable to ef- fectively

For the Text-Based Fake News Detection subtask, we proposed a neural network that combines textual features encoded by a pre-trained BERT model and metadata of tweets encoded by

FACT (Factuality Analysis and Classification Task) is a task to classify events in Span- ish texts (from Spanish and Uruguayan newspaper), according to their factuality status..

TimeNet-based features have been shown to be useful for various tasks including ECG classification [Malhotra et al., 2017]. In this work, we consider application of TimeNet

Trained Query Node is an extension of Behavior Trees that allow training some parts of Behavior Tree by demonstration using dif- ferent techniques to make the behavior model.. This

The results, illustrated in Figure 5, show an increased number of significant responses (in favour of the consistent grammar) for the extended test of ABA familiarization: the

The approach is evaluated in the context of the LifeCLEF 2016 bird identification task - an open challenge conducted on a dataset containing 34 128 audio recordings representing