224 citations · 650 across the 25 of their papers we have counts for
7 papers · 1 filter
Shift-Invariant Kernel Additive Modelling for Audio Source Separation
Delia Fano Yela, Sebastian Ewert, Ken O'Hanlon +1
A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of-the-art approaches are supervised methods t…
A Tutorial on Deep Learning for Music Information Retrieval
Keunwoo Choi, György Fazekas, Kyunghyun Cho +1
Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the…
A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging
Keunwoo Choi, György Fazekas, Kyunghyun Cho +1
In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks. We perform comprehensive experiments involving audio preproc…
An Augmented Lagrangian Method for Piano Transcription using Equal Loudness Thresholding and LSTM-based Decoding
Sebastian Ewert, Mark B. Sandler
A central goal in automatic music transcription is to detect individual note events in music recordings. An important variant is instrument-dependent music transcription where meth…
The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging
Keunwoo Choi, George Fazekas, Kyunghyun Cho +1
Deep neural networks (DNN) have been successfully applied to music classification including music tagging. However, there are several open questions regarding the training, evaluat…
Transfer learning for music classification and regression tasks
Keunwoo Choi, György Fazekas, Mark Sandler +1
In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector…