Keunwoo Choi | Semantic Scholar

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Automatic Tagging Using Deep Convolutional Neural Networks

Keunwoo Choi, George Fazekas, Mark B. Sandler
ISMIR
2016

We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional layers and… (More)

Convolutional recurrent neural networks for music classification

Keunwoo Choi, George Fazekas, Mark B. Sandler, Kyunghyun Cho
IEEE International Conference on Acoustics…
2016

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks… (More)

Transfer Learning for Music Classification and Regression Tasks

Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho
ISMIR
2017

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 using the… (More)

Text-based LSTM networks for Automatic Music Composition

Keunwoo Choi, George Fazekas, Mark B. Sandler
ArXiv
2016

In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn… (More)

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

We introduce Kapre, Keras layers for audio and music signal preprocessing. Music research using deep neural networks requires a heavy and tedious preprocessing stage, for which audio processing… (More)

Explaining Deep Convolutional Neural Networks on Music Classification

Keunwoo Choi, George Fazekas, Mark B. Sandler
ArXiv
2016

Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e.g. genre classification, mood detection, and chord recognition. However, the… (More)

A Tutorial on Deep Learning for Music Information Retrieval

Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark B. Sandler
ArXiv
2017

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 majority of works… (More)

DLR : Toward a deep learned rhythmic representation for music content analysis

Yeonwoo Jeong, Keunwoo Choi, Hosan Jeong
ArXiv
2017

In the use of deep neural networks, it is crucial to provide appropriate input representations for the network to learn from. In this paper, we propose an approach to learn a representation that… (More)

AURALISATION OF DEEP CONVOLUTIONAL NEURAL NETWORKS : LISTENING TO LEARNED FEATURES

Keunwoo Choi, George Fazekas, Mark Sandler
2015

Deep learning has been actively adopted in the field of music information retrieval, e.g. genre classification, mood detection, and chord recognition. Deep convolutional neural networks (CNNs), one… (More)

Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

Keunwoo Choi, George Fazekas, Mark B. Sandler
UMAP
2016

We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between… (More)