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Mark B. Sandler

Queen Mary University of London

4 papers hereh-index 4810.3k citations487 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI2
  • cs.LG1
  • cs.SD1
affiliations
  • Queen Mary University of London

identity via Semantic Scholar / OpenAlex

most citedExplaining Deep Convolutional Neural Networks on Music Classification

32 citations · 32 across the 1 of their papers we have counts for

collaborators

4 papers

cs.LG2016★ 32 cited

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.…

cs.AI2016

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…

cs.SD2016

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…

cs.AI2016

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…

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