Imagine standing on a street corner in the city. With your eyes closed you can hear and
recognize a succession of sounds: cars passing by, people speaking, their footsteps when …

High‐throughput environmental sensing technologies are increasingly central to global
monitoring of the ecological impacts of human activities. In particular, the recent boom in …

The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-
temporal patterns makes them well suited to environmental sound classification. However …

We learn rich natural sound representations by capitalizing on large amounts of unlabeled
sound data collected in the wild. We leverage the natural synchronization between vision …

In this paper, we present an end-to-end approach for environmental sound classification
based on a 1D Convolution Neural Network (CNN) that learns a representation directly from …

This paper evaluates the potential of convolutional neural networks in classifying short audio
clips of environmental sounds. A deep model consisting of 2 convolutional layers with max …

The visual landscape plays a pivotal role in urban planning and healthy cities. Recent
studies of visual evaluation focus on either objective or subjective approach, while …

This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale
detection of sound events using weakly labeled data (without time boundaries). The target of …

Acoustic scene classification (ASC) has gained significant interest recently due to its diverse
applications. Various audio signal processing and machine learning methods have been …

Cognitive prediction in the complicated and active environments is of great importance role
in artificial learning. Classification accuracy of sound events has a robust relation with the …