45 citations · 195 across the 44 of their papers we have counts for
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Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags
Xavier Favory, Konstantinos Drossos, Tuomas Virtanen +1
Self-supervised audio representation learning offers an attractive alternative for obtaining generic audio embeddings, capable to be employed into various downstream tasks. Publish…
WaveTransformer: A Novel Architecture for Audio Captioning Based on Learning Temporal and Time-Frequency Information
An Tran, Konstantinos Drossos, Tuomas Virtanen
Automated audio captioning (AAC) is a novel task, where a method takes as an input an audio sample and outputs a textual description (i.e. a caption) of its contents. Most AAC meth…
Conditioned Time-Dilated Convolutions for Sound Event Detection
Konstantinos Drossos, Stylianos I. Mimilakis, Tuomas Virtanen
Sound event detection (SED) is the task of identifying sound events along with their onset and offset times. A recent, convolutional neural networks based SED method, proposed the…
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…
COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations
Xavier Favory, Konstantinos Drossos, Tuomas Virtanen +1
Audio representation learning based on deep neural networks (DNNs) emerged as an alternative approach to hand-crafted features. For achieving high performance, DNNs often need a la…
Depthwise Separable Convolutions Versus Recurrent Neural Networks for Monaural Singing Voice Separation
Pyry Pyykkönen, Styliannos I. Mimilakis, Konstantinos Drossos +1
Recent approaches for music source separation are almost exclusively based on deep neural networks, mostly employing recurrent neural networks (RNNs). Although RNNs are in many cas…