8 citations · 13 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
Revisiting Representation Learning for Singing Voice Separation with Sinkhorn Distances
Stylianos Ioannis Mimilakis, Konstantinos Drossos, Gerald Schuller
In this work we present a method for unsupervised learning of audio representations, focused on the task of singing voice separation. We build upon a previously proposed method for…
Unsupervised Interpretable Representation Learning for Singing Voice Separation
Stylianos I. Mimilakis, Konstantinos Drossos, Gerald Schuller
In this work, we present a method for learning interpretable music signal representations directly from waveform signals. Our method can be trained using unsupervised objectives an…
Sound Event Detection with Depthwise Separable and Dilated Convolutions
Konstantinos Drossos, Stylianos I. Mimilakis, Shayan Gharib +2
State-of-the-art sound event detection (SED) methods usually employ a series of convolutional neural networks (CNNs) to extract useful features from the input audio signal, and the…