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Environment Transfer for Distributed Systems

arXiv 2101.01863 ↗First posted 6 Jan 2021cs.SD cs.LG eess.AScited by 0

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Abstract (arXiv)

Collecting sufficient amount of data that can represent various acoustic environmental attributes is a critical problem for distributed acoustic machine learning. Several audio data augmentation techniques have been introduced to address this problem but they tend to remain in simple manipulation of existing data and are insufficient to cover the variability of the environments. We propose a method to extend a technique that has been used for transferring acoustic style textures between audio data. The method transfers audio signatures between environments for distributed acoustic data augmentation. This paper devises metrics to evaluate the generated acoustic data, based on classification accuracy and content preservation. A series of experiments were conducted using UrbanSound8K dataset and the results show that the proposed method generates better audio data with transferred environmental features while preserving content features.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 6 Jan 2021.

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18 of 42 references.

References · 18

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
  2. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,359
  3. 2016CNN Architectures for Large-Scale Audio Classificationcited by 702
  4. 2015A Neural Algorithm of Artistic Stylecited by 481
  5. 2018Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesiscited by 281
  6. 2017Style Transfer from Non-Parallel Text by Cross-Alignmentcited by 247
  7. 2016SoundNet: Learning Sound Representations from Unlabeled Videocited by 244
  8. 2018Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfercited by 191
  9. 2017Style Transfer in Text: Exploration and Evaluationcited by 146
  10. 2016SampleRNN: An Unconditional End-to-End Neural Audio Generation Modelcited by 110
  11. 2017Regularization for Deep Learning: A Taxonomycited by 91
  12. 2016Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classificationcited by 84
6 more
  1. 2017Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Modelscited by 57
  2. 2020Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 43
  3. 2017Audio style transfercited by 15
  4. 2016Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordingscited by 8
  5. 2016Texture Synthesis Using Shallow Convolutional Networks with Random Filterscited by 4
  6. 2018Autoencoder Based Architecture For Fast & Real Time Audio Style Transfercited by 1

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