8 citations · 13 across the 11 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…