activity
20172020
most citedMulti-task Regularization Based on Infrequent Classes for Audio Captioning

18 citations · 18 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SD202018 cited

Multi-task Regularization Based on Infrequent Classes for Audio Captioning

Emre Çakır, Konstantinos Drossos, Tuomas Virtanen

Audio captioning is a multi-modal task, focusing on using natural language for describing the contents of general audio. Most audio captioning methods are based on deep neural netw…

eess.AS2018

Unsupervised adversarial domain adaptation for acoustic scene classification

Shayan Gharib, Konstantinos Drossos, Emre Çakir +2

A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed…

cs.SD2018

End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input

Emre Çakır, Tuomas Virtanen

Sound event detection systems typically consist of two stages: extracting hand-crafted features from the raw audio waveform, and learning a mapping between these features and the t…

cs.SD2017

Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection

Sharath Adavanne, Konstantinos Drossos, Emre Çakır +1

This paper studies the detection of bird calls in audio segments using stacked convolutional and recurrent neural networks. Data augmentation by blocks mixing and domain adaptation…

cs.SD2017

Convolutional Recurrent Neural Networks for Bird Audio Detection

EmreÇakır, Sharath Adavanne, Giambattista Parascandolo +2

Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose…