Crowdsourcing is a popular tool for collecting large amounts of annotated data, but the
specific format of the strong labels necessary for sound event detection is not easily …

'Soundscapes' are an increasingly active topic in Human-Computer Interaction (HCI) and
interaction design. From map** acoustic environments through sound recordings to …

Abstract The quality of Machine Learning (ML) applications is commonly assessed by
quantifying how well an algorithm fits its respective training data. Yet, a perfect model that …

In this paper, we study the use of soft labels to train a system for sound event detection
(SED). Soft labels can result from annotations which account for human uncertainty about …

Deep learning work on musical instrument recognition has generally focused on instrument
classes for which we have abundant data. In this work, we exploit hierarchical relationships …

SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and
evaluation of machine listening systems for real-world urban noise monitoring. It consists of …

Crowdsourcing has become a common approach for annotating large amounts of data. It
has the advantage of harnessing a large workforce to produce large amounts of data in a …

Noise pollution is one of the topmost quality of life issues for urban residents in the United
States. Continued exposure to high levels of noise has proven effects on health, including …

Data annotation interfaces predominantly leverage ground truth labels to guide annotators
toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain …

We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal
information. This dataset is aimed for the development and evaluation of machine listening …