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Matthew C. McCallum
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2020 – today
- 2024
- [c12]Matthew C. McCallum, Matthew E. P. Davies, Florian Henkel, Jaehun Kim, Samuel E. Sandberg:
On The Effect Of Data-Augmentation On Local Embedding Properties In The Contrastive Learning Of Music Audio Representations. ICASSP 2024: 671-675 - [c11]Matthew C. McCallum, Florian Henkel, Jaehun Kim, Samuel E. Sandberg, Matthew E. P. Davies:
Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search. ICASSP 2024: 686-690 - [c10]Florian Henkel, Jaehun Kim, Matthew C. McCallum, Samuel E. Sandberg, Matthew E. P. Davies:
Tempo Estimation as Fully Self-Supervised Binary Classification. ICASSP 2024: 1356-1360 - [i7]Matthew C. McCallum, Matthew E. P. Davies, Florian Henkel, Jaehun Kim, Samuel E. Sandberg:
On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations. CoRR abs/2401.08889 (2024) - [i6]Florian Henkel, Jaehun Kim, Matthew C. McCallum, Samuel E. Sandberg, Matthew E. P. Davies:
Tempo estimation as fully self-supervised binary classification. CoRR abs/2401.08891 (2024) - [i5]Matthew C. McCallum, Florian Henkel, Jaehun Kim, Samuel E. Sandberg, Matthew E. P. Davies:
Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search. CoRR abs/2401.08902 (2024) - 2022
- [c9]Mojtaba Heydari, Matthew C. McCallum, Andreas F. Ehmann, Zhiyao Duan:
A Novel 1D State Space for Efficient Music Rhythmic Analysis. ICASSP 2022: 421-425 - [c8]Matthew C. McCallum, Filip Korzeniowski, Sergio Oramas, Fabien Gouyon, Andreas F. Ehmann:
Supervised and Unsupervised Learning of Audio Representations for Music Understanding. ISMIR 2022: 256-263 - [i4]Matthew C. McCallum, Filip Korzeniowski, Sergio Oramas, Fabien Gouyon, Andreas F. Ehmann:
Supervised and Unsupervised Learning of Audio Representations for Music Understanding. CoRR abs/2210.03799 (2022) - 2021
- [i3]Matthew C. McCallum:
Unsupervised Learning of Deep Features for Music Segmentation. CoRR abs/2108.12955 (2021) - [i2]Mojtaba Heydari, Matthew C. McCallum, Andreas F. Ehmann, Zhiyao Duan:
A Novel 1D State Space for Efficient Music Rhythmic Analysis. CoRR abs/2111.00704 (2021) - 2020
- [c7]Filip Korzeniowski, Oriol Nieto, Matthew C. McCallum, Minz Won, Sergio Oramas, Erik M. Schmidt:
Mood Classification Using Listening Data. ISMIR 2020: 542-549 - [i1]Filip Korzeniowski, Oriol Nieto, Matthew C. McCallum, Minz Won, Sergio Oramas, Erik M. Schmidt:
Mood Classification Using Listening Data. CoRR abs/2010.11512 (2020)
2010 – 2019
- 2019
- [c6]Matthew C. McCallum:
Unsupervised Learning of Deep Features for Music Segmentation. ICASSP 2019: 346-350 - [c5]Oriol Nieto, Matthew C. McCallum, Matthew E. P. Davies, Andrew Robertson, Adam M. Stark, Eran Egozy:
The Harmonix Set: Beats, Downbeats, and Functional Segment Annotations of Western Popular Music. ISMIR 2019: 565-572 - 2018
- [c4]Matthew C. McCallum:
Foreground Harmonic Noise Reduction for Robust Audio Fingerprinting. ICASSP 2018: 3146-3150 - 2013
- [j1]Matthew C. McCallum, Bernard J. Guillemin:
Stochastic-Deterministic MMSE STFT Speech Enhancement With General A Priori Information. IEEE Trans. Speech Audio Process. 21(7): 1445-1457 (2013) - [c3]Matthew C. McCallum, Bernard J. Guillemin:
Joint stochastic-deterministic wiener filtering with recursive Bayesian estimation of deterministic speech. INTERSPEECH 2013: 460-464 - [c2]Matthew C. McCallum, Bernard J. Guillemin:
Stochastic-deterministic signal modelling for the tracking of pitch in noise and speech mixtures using factorial HMMs. INTERSPEECH 2013: 3289-3293 - 2012
- [c1]Matthew C. McCallum, Bernard J. Guillemin:
Accounting for deterministic noise components in a MMSE STSA speech enhancement framework. ISCIT 2012: 169-174
Coauthor Index
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