45 citations · 117 across the 18 of their papers we have counts for
9 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…
Temporal Sub-sampling of Audio Feature Sequences for Automated Audio Captioning
Khoa Nguyen, Konstantinos Drossos, Tuomas Virtanen
Audio captioning is the task of automatically creating a textual description for the contents of a general audio signal. Typical audio captioning methods rely on deep neural networ…
Multichannel Singing Voice Separation by Deep Neural Network Informed DOA Constrained CNMF
Antonio J. Muñoz-Montoro, Julio J. Carabias-Orti, Archontis Politis +1
This work addresses the problem of multichannel source separation combining two powerful approaches, multichannel spectral factorization with recent monophonic deep-learning (DL) b…
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 via dilated convolutional recurrent neural networks
Yanxiong Li, Mingle Liu, Konstantinos Drossos +1
Convolutional recurrent neural networks (CRNNs) have achieved state-of-the-art performance for sound event detection (SED). In this paper, we propose to use a dilated CRNN, namely…
Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters
Niccoló Nicodemo, Gaurav Naithani, Konstantinos Drossos +2
Effective employment of deep neural networks (DNNs) in mobile devices and embedded systems is hampered by requirements for memory and computational power. This paper presents a non…