Penjernihan Derau pada Suara Kanal Tunggal dengan Pembelajaran Faktorisasi Matriks Non-negatif tanpa Pengawasan

  • Tirtadwipa Manunggal PT. Bahasa Kinerja Utama
  • Oskar Riandi PT. Bahasa Kinerja Utama
  • Ardhi Ma’arik PT. Bahasa Kinerja Utama
  • Lalan Suryantoro PT. Bahasa Kinerja Utama
  • Achmad Satria Putera PT. Bahasa Kinerja Utama
  • Izzul Al-Hakam PT. Bahasa Kinerja Utama


This article examines an approach of denoising method on single channel using Non-negative Matrix Factorization (NMF)  on unsupervised-learning scheme. This technique utilizes the property of NMF which unravels spectrogram matrices of noise-interfered speech and noise   itself into their building-block vector. As extension for NMF, Wiener filter is applied in the end of steps. This method is designated to run in low latency system, hence preparing certain noise model for particular condition beforehand is impractical. Thus the noise model is taken automatically from the unvoiced part of noise-interfered speech. The contribution achieved in this research is the kind of NMF learning using linear and non-linear constraint which is done without explicitly providing noise models. Therefore the denoising process could be undergone flexibly in any noise condition.


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How to Cite
MANUNGGAL, Tirtadwipa et al. Penjernihan Derau pada Suara Kanal Tunggal dengan Pembelajaran Faktorisasi Matriks Non-negatif tanpa Pengawasan. Jurnal Linguistik Komputasional, [S.l.], v. 1, n. 1, p. 1 - 10, mar. 2018. ISSN 2621-9336. Available at: <>. Date accessed: 31 mar. 2020. doi: