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researcher

S. Dixon

27 papers hereh-index 499.7k citations237 works total

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

author position
  • middle author6
  • last author20

Across the 26 of 27 papers where every author was matched, so the position is known.

fields
  • cs.SD15
  • cs.IR3
  • cs.LG3
  • eess.AS3
  • cs.AI1
  • cs.MM1
same name
  • S. Dixon — 22 papers, h 9
  • S. Dixon — 10 papers, h 9
  • S. Dixon — 5 papers, h 4
  • S. Dixon — 4 papers, h 8
  • S. Dixon — 2 papers, h 2
  • S. Dixon — 1 paper, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142023
most citedWave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation

75 citations · 156 across the 22 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.SD2020

Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment

Ruchit Agrawal, Simon Dixon

Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time War…

eess.AS2020★ 2 cited

A Hybrid Approach to Audio-to-Score Alignment

Ruchit Agrawal, Simon Dixon

Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time War…

eess.AS2020

Automatic Lyrics Transcription using Dilated Convolutional Neural Networks with Self-Attention

Emir Demirel, Sven Ahlback, Simon Dixon

Speech recognition is a well developed research field so that the current state of the art systems are being used in many applications in the software industry, yet as by today, th…

eess.AS2020

Reliable Local Explanations for Machine Listening

Saumitra Mishra, Emmanouil Benetos, Bob L. Sturm +1

One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions. Sensitivity anal…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.