224 citations · 767 across the 33 of their papers we have counts for
6 papers · 1 filter
Convolutional Recurrent Neural Networks for Music Classification
Keunwoo Choi, George Fazekas, Mark Sandler +1
We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurr…
Towards Music Captioning: Generating Music Playlist Descriptions
Keunwoo Choi, George Fazekas, Brian McFee +2
Descriptions are often provided along with recommendations to help users' discovery. Recommending automatically generated music playlists (e.g. personalised playlists) introduces t…
Explaining Deep Convolutional Neural Networks on Music Classification
Keunwoo Choi, George Fazekas, Mark Sandler
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.…
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions
Keunwoo Choi, George Fazekas, Mark Sandler
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 transi…
Automatic tagging using deep convolutional neural networks
Keunwoo Choi, George Fazekas, Mark Sandler
We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional la…
Text-based LSTM networks for Automatic Music Composition
Keunwoo Choi, George Fazekas, Mark Sandler
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 t…