Audio Tagging using Linear Noise Modelling Layer
Shubhr Singh, Arjun Pankajakshan, Emmanouil Benetos · 2019
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.