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

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

One of the obstacles in research activities concentrating on environmental sound
classification is the scarcity of suitable and publicly available datasets. This paper tries to …

We introduce TUT Acoustic Scenes 2016 database for environmental sound research,
consisting of binaural recordings from 15 different acoustic environments. A subset of this …

This paper presents and discusses various metrics proposed for evaluation of polyphonic
sound event detection systems used in realistic situations where there are typically multiple …

For intelligent systems to make best use of the audio modality, it is important that they can
recognize not just speech and music, which have been researched as specific tasks, but …

This paper describes Task 2 of the DCASE 2018 Challenge, titled" General-purpose audio
tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle …

Sound event detection (SED) is a task to detect sound events in an audio recording. One
challenge of the SED task is that many datasets such as the Detection and Classification of …

Presents our entry to the Detection and Classification of Acoustic Scenes challenge. The
approach we propose for classifying acoustic scenes is based on transforming the audio …

Deep neural networks (DNNs) have recently achieved a great success in various learning
task, and have also been used for classification of environmental sounds. While DNNs are …