8 citations · 13 across the 11 of their papers we have counts for
16 papers
End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models
Lauri Juvela, Eero-Pekka Damskägg, Aleksi Peussa +4
This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the f…
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection
David S. Johnson, Wolfgang Lorenz, Michael Taenzer +4
Research on sound event detection (SED) in environmental settings has seen increased attention in recent years. The large amounts of (private) domestic or urban audio data needed r…
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…
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…
Harmonic-Percussive Source Separation with Deep Neural Networks and Phase Recovery
Konstantinos Drossos, Paul Magron, Stylianos Ioannis Mimilakis +1
Harmonic/percussive source separation (HPSS) consists in separating the pitched instruments from the percussive parts in a music mixture. In this paper, we propose to apply the rec…
An Overview of Lead and Accompaniment Separation in Music
Zafar Rafii, Antoine Liutkus, Fabian-Robert Stöter +3
Popular music is often composed of an accompaniment and a lead component, the latter typically consisting of vocals. Filtering such mixtures to extract one or both components has m…
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…
MaD TwinNet: Masker-Denoiser Architecture with Twin Networks for Monaural Sound Source Separation
Konstantinos Drossos, Stylianos Ioannis Mimilakis, Dmitriy Serdyuk +3
Monaural singing voice separation task focuses on the prediction of the singing voice from a single channel music mixture signal. Current state of the art (SOTA) results in monaura…
Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask
Stylianos Ioannis Mimilakis, Konstantinos Drossos, João F. Santos +3
Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a learnable function or is not encapsulated…
A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation
Stylianos Ioannis Mimilakis, Konstantinos Drossos, Tuomas Virtanen +1
The objective of deep learning methods based on encoder-decoder architectures for music source separation is to approximate either ideal time-frequency masks or spectral representa…
Investigating the Potential of Pseudo Quadrature Mirror Filter-Banks in Music Source Separation Tasks
Stylianos Ioannis Mimilakis, Gerald Schuller
Estimating audio and musical signals from single channel mixtures often, if not always, involves a transformation of the mixture signal to the time-frequency (T-F) domain in which…