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Deep learning models to remix music for cochlear implant users.

Tom Gajęcki1, Waldo Nogueira1

  • 1Department of Otolaryngology, Medical University Hannover and Cluster of Excellence Hearing4all, Hannover, 30625, Germany.

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Summary

This study enhances music for cochlear implant (CI) users by separating vocals using source separation (SS) algorithms. A Multilayer Perceptron (MLP) algorithm is proposed for real-time music remixing, improving enjoyment for CI recipients.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Auditory Neuroscience

Background:

  • Cochlear implants (CI) help individuals with severe hearing loss but struggle with complex audio like music.
  • Enhancing lead vocals in music improves music enjoyment for CI users.

Purpose of the Study:

  • To evaluate source separation (SS) algorithms for remixing pop music by adjusting vocal gain.
  • To identify a suitable algorithm for real-time music processing for CI users.

Main Methods:

  • Employed deep convolutional auto-encoders, recurrent neural networks, Multilayer Perceptron (MLP), and non-negative matrix factorization for SS.
  • Conducted objective and subjective perceptual experiments with normal-hearing and CI participants.
  • Assessed algorithm performance based on introduced artifacts and computation time for real-time feasibility.

Main Results:

  • The Multilayer Perceptron (MLP) demonstrated robust performance across tested data.
  • MLP introduced minimal distortions and artifacts, imperceptible to CI users.
  • MLP showed promise for real-time monaural audio source separation.

Conclusions:

  • The Multilayer Perceptron (MLP) is a viable algorithm for real-time music remixing for CI users.
  • MLP effectively enhances music perception and enjoyment in CI recipients.
  • This approach offers a pathway to improved auditory experiences for individuals with hearing impairments.