5 accepted papers
We introduce the new audio analysis task of pedestrian detection and present a new large-scale dataset for this task. While the preliminary results prove the viability of using audio approaches for pedestrian detection, they also show that this challenging task cannot be easily solved with standard…
Spatial audio quality is a highly multifaceted concept, with many interactions between environmental, geometrical, anatomical, psychological, and contextual factors. Methods for characterization or evaluation of the geometrical components of spatial audio quality, however, remain scarce, despite bei…
Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method fo…
We propose a deep learning approach to predicting audio event onsets in electroencephalogram (EEG) recorded from users as they listen to music. We use a publicly available dataset containing ten contemporary songs and concurrently recorded EEG. We generate a sequence of onset labels for the songs in…
Deep architectures have become ubiquitous in Music Information Retrieval (MIR) tasks, however, concurrent studies still lack a deep understanding of the input properties being evaluated by the networks. In this study, we show by the example of a Music Genre Classification system the potential depend…