Laure Prétet

Signal Processing · 2019

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom Laure Prétet trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Naoya Takahashi
  2. Antoine Liutkus
  3. Chao-Ling Hsu
  4. Rachel M. Bittner
  5. Fabian-Robert Stöter
  6. Aditya Arie Nugraha
  7. Daniel Stoller
  8. Yuki Mitsufuji
  9. Andreas Jansson
  10. Stefan Uhlich

Who trusts Laure Prétet overall

  1. Romain Hennequin
  2. Alexandre D'efossez
  3. Anis Khlif
  4. Félix Voituret
  5. Nicolas Usunier
  6. Manuel Moussallam

Papers from Laure Prétet's perspective

Ordered by how strongly the researchers Laure Prétet trusts endorse each paper. Same corpus, a different order for every researcher.

  1. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  2. The 2018 Signal Separation Evaluation Campaign
  3. Improving music source separation based on deep neural networks through data augmentation and network blending
  4. MUSDB18 - a corpus for music separation
  5. MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research (International Society for Music Information Retrieval Conference)
  6. Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
  7. Multi-Scale multi-band densenets for audio source separation
  8. Singing Voice Separation with Deep U-Net Convolutional Networks
  9. Monoaural Audio Source Separation Using Deep Convolutional Neural Networks
  10. On the Improvement of Singing Voice Separation for Monaural Recordings Using the MIR-1K Dataset (IEEE Transactions on Audio, Speech, and Language Processing)

Their papers