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Removing Lavalier Microphone Rustle With Recurrent Neural Networks.pdf

WichernLukin18.pdf
Preview of Removing lavalier microphone rustle with recurrent neural networks
🔗 Source: audio.rightmark.org
📊 Size: 746 KB
👤 Author: Gordon Wichern, Alexey Lukin
⬇️ Downloads: 146

Summary

Researchers from iZotope, Inc. presented a paper on removing lavalier microphone rustle using recurrent neural networks at the 21st International Conference on Digital Audio Effects. Lavalier microphone rustle is a challenging problem to solve due to its non-stationary nature and overlap with speech in time and frequency. The authors applied neural network speech separation techniques to remove rustle noise and compared the performance of different deep network architectures and input spectral resolutions. They found that bidirectional recurrent networks with a spectral resolution of around 20 Hz performed best. The authors also proposed an ambience preservation post-processing step to minimize potential gating artifacts during pauses in speech.

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Researchers from iZotope, Inc.

Technical Information

  • File Format: PDF
  • File Size: 746 KB
  • Pages: 7
  • Language: EN
  • Author: Gordon Wichern, Alexey Lukin
  • Total Downloads: 146
  • Last Updated: 6 days ago

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