8 citations · 13 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
SignalTrain: Profiling Audio Compressors with Deep Neural Networks
Scott H. Hawley, Benjamin Colburn, Stylianos I. Mimilakis
In this work we present a data-driven approach for predicting the behavior of (i.e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect"). Our o…
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation
Stylianos Ioannis Mimilakis, Konstantinos Drossos, Estefanía Cano +1
The goal of this work is to investigate what singing voice separation approaches based on neural networks learn from the data. We examine the mapping functions of neural networks b…
Close Miking Empirical Practice Verification: A Source Separation Approach
Konstantinos Drossos, Stylianos Ioannis Mimilakis, Andreas Floros +2
Close miking represents a widely employed practice of placing a microphone very near to the sound source in order to capture more direct sound and minimize any pickup of ambient so…