ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2108.12955
  4. Cited By
Unsupervised Learning of Deep Features for Music Segmentation

Unsupervised Learning of Deep Features for Music Segmentation

30 August 2021
Matthew C. McCallum
    SSL
ArXivPDFHTML

Papers citing "Unsupervised Learning of Deep Features for Music Segmentation"

15 / 15 papers shown
Title
Why Perturbing Symbolic Music is Necessary: Fitting the Distribution of
  Never-used Notes through a Joint Probabilistic Diffusion Model
Why Perturbing Symbolic Music is Necessary: Fitting the Distribution of Never-used Notes through a Joint Probabilistic Diffusion Model
Shipei Liu
Xiaoya Fan
Guowei Wu
DiffM
34
1
0
04 Aug 2024
On the Effect of Data-Augmentation on Local Embedding Properties in the
  Contrastive Learning of Music Audio Representations
On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations
Matthew C. McCallum
Matthew E. P. Davies
Florian Henkel
Jaehun Kim
Samuel E. Sandberg
33
6
0
17 Jan 2024
Barwise Music Structure Analysis with the Correlation Block-Matching
  Segmentation Algorithm
Barwise Music Structure Analysis with the Correlation Block-Matching Segmentation Algorithm
Axel Marmoret
Jérémy E. Cohen
Frédéric Bimbot
21
0
0
30 Nov 2023
Self-Similarity-Based and Novelty-based loss for music structure
  analysis
Self-Similarity-Based and Novelty-based loss for music structure analysis
Geoffroy Peeters
23
2
0
05 Sep 2023
Pitchclass2vec: Symbolic Music Structure Segmentation with Chord
  Embeddings
Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings
Nicolas Lazzari
Andrea Poltronieri
Valentina Presutti
39
6
0
24 Mar 2023
SSM-Net: feature learning for Music Structure Analysis using a
  Self-Similarity-Matrix based loss
SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss
Geoffroy Peeters
Florian Angulo
19
1
0
15 Nov 2022
Self-Supervised Hierarchical Metrical Structure Modeling
Self-Supervised Hierarchical Metrical Structure Modeling
Junyan Jiang
Gus Xia
36
2
0
31 Oct 2022
Convolutive Block-Matching Segmentation Algorithm with Application to
  Music Structure Analysis
Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis
Axel Marmoret
Jérémy E. Cohen
France
27
1
0
27 Oct 2022
Supervised and Unsupervised Learning of Audio Representations for Music
  Understanding
Supervised and Unsupervised Learning of Audio Representations for Music Understanding
Matthew C. McCallum
Filip Korzeniowski
Sergio Oramas
F. Gouyon
Andreas F. Ehmann
SSL
80
37
0
07 Oct 2022
Learning Hierarchical Metrical Structure Beyond Measures
Learning Hierarchical Metrical Structure Beyond Measures
Junyan Jiang
Daniel Y. Chin
Yixiao Zhang
Gus Xia
39
4
0
21 Sep 2022
The Power of Fragmentation: A Hierarchical Transformer Model for
  Structural Segmentation in Symbolic Music Generation
The Power of Fragmentation: A Hierarchical Transformer Model for Structural Segmentation in Symbolic Music Generation
Guowei Wu
Shipei Liu
Xiaoya Fan
22
12
0
17 May 2022
Barwise Compression Schemes for Audio-Based Music Structure Analysis
Barwise Compression Schemes for Audio-Based Music Structure Analysis
Axel Marmoret
Jérémy E. Cohen
Frédéric Bimbot
28
3
0
10 Feb 2022
Exploring single-song autoencoding schemes for audio-based music
  structure analysis
Exploring single-song autoencoding schemes for audio-based music structure analysis
Axel Marmoret
Jérémy E. Cohen
Frédéric Bimbot
30
0
0
27 Oct 2021
Supervised Metric Learning for Music Structure Features
Supervised Metric Learning for Music Structure Features
Ju-Chiang Wang
Jordan B. L. Smith
Weiyi Lu
Xuchen Song
19
17
0
18 Oct 2021
Music Boundary Detection using Convolutional Neural Networks: A
  comparative analysis of combined input features
Music Boundary Detection using Convolutional Neural Networks: A comparative analysis of combined input features
Carlos Hernandez-Olivan
J. R. Beltrán
David Diaz-Guerra
SSL
11
14
0
17 Aug 2020
1