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AUDIO SIGNAL SEPERATION WITH SINGLE CHANNEL BLIND SOURCE SEPERATION

Year 2019, Volume: 14 Issue: 1, 26 - 38, 31.01.2019

Abstract

      Blind source separation
can be defined as estimating each source that makes up this mixture from a data
set containing a mixture of more than one signal. Calling this process as blind
specifies that there is no additional information about resources. Since it has
as many signals as the number of signals to be estimated on the blind source separation,
estimating sources is a cost process because of there is only one mixing signal
on the single-channel blind source separation process. An unwanted noise
attached to any signal can be separated from the source signal thanks to said
method. Similarly two different instrumental signal sources recorded with a
single microphone can be separated from each other as in this study. In this
study, two signals recorded as single channel were separated from each other
with non-negative matrix separation using continuous wavelet transform. The
results were evaluated in terms of signal noise ratio and signal distortion
ratio to evaluate the performance analysis of the proposed method. 

References

  • [1] Niknazar, M., (2014). Blind Source Separation of Underdetermined Mixtures Of Event-Related Sources. Signal Processessing: Cilt:101, Sayı:8, ss:52–64.
  • [2] Nikunen, J.T. and Virtanen, T., (2014). Direction of Arrival Based Spatial Covariance Model for Blind Sound Source Separation IEEE/ACM Trans. Audio Speech Lang. Process., Cilt:22, Sayı:3, ss:727-739.
  • [3] Pengju, H., Tingting, S., Wenhui, L., and Weibiao, Y., (2018). Single Channel Blind Source Separation on The Instantaneous Mixed Signal Of Multiple Dynamic Sources. Mechanical Systems and Signal Processing. Cilt:113, ss:22-35.
  • [4] Smaragdis, P., (2004). Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sourses from Monophonic Inputs. Lecture Notes in Computer Science, Cilt:319, Sayı:5, ss:494–499.
  • [5] Schmidt, M.N. and Olsson, R.K., (2006). Singlechannel Speech Separation Using Sparse Non-negative Matrix Factorization, International Conference on Spoken Language Processing (INTERSPEECH), Glasgow.
  • [6] Kırbız, S. ve Günsel, B., (2009). Negatif Olmayan Matris Ayrıştırma ile Tek-Kanaldan Algısal Ses Ayrıştırma, IEEE, Antalya.
  • [7] He, P., She, T., Li, W., and Yuan, W., (2018). Single Channel Blind Source Separation on The Instantaneous Mixed Signal of Multiple Dynamic Sources. Mechanical Systems and Signal Processing. Sayı:113, ss: 22-35.
  • [8] Schmidt, M.N., (2008). Single-Channel Source Separation Using Non-negative Matrix Factorization, Doktora Tezi, Technical University of Denmark.
  • [9] Mateo, C. and Talavera, J.A., (2018). Short-time Fourier transform with the window size fixed in the frequency domain. Digital Signal Processing. Cilt:77, ss:13-21.
  • [10] Mateo, C. and Talavera, J.A., (2018). Short-Time Fourier Transform with the Window Size Fixed in the Frequency Domain (STFT-FD): Implementation. SoftwareX, Cilt:8, ss:5-8.
  • [11] Choukri, D. and Mehdi, A., (2017). A New Data-Driven Deep Learning Model for Pattern Categorization using Fast Independent Component Analysis and Radial Basis Function Network. Taking Social Networks resources as a case Procedia Computer Science, Cilt:113, ss:97-104.
  • [12] Vanluyten, B., Jan, C., and Bart De Moor, B.M., (2008). Structured Nonnegative Matrix Factorization with Applications to Hidden Markov Realization and Clustering. Linear Algebra and its Applications, Cilt:429, Sayı:7, ss:1409-1424.
  • [13] Cardoso, J.F. and Comon, P., (1996). Independent Component Analysis, A Survey of Some Algebraic Methods, Proc. ISCAS96, ss:93-96.
  • [14] Comon, P., (1994). Independent Component Analysis: A New Concept, Signal Processing, 36, 287-314.
  • [15] O’Grady, P.D., Pearlmutter, B.A., and Rickard, S.T., (2005). Survey of Sparse and Non-sparse Methods in Source Separation, IJIST International Journal of Imaging Systems and Technology, 18, ss: 78-85.
  • [16] Kanailal Mahato K., (2018). the composition of fractional hankel wavelet transform on some function spaces. Applied Mathematics and Computation, Cilt:337, ss:76-86.
  • [17] Dai, H., Zheng, Z., Wang, W., (2018). A new ractional wavelet transform. Communications in Nonlinear Science and Numerical Simulation, Sayı:44, ss:19-36.
  • [18] Lee, D. and Seung, H., (1999). Learning the Parts of Objects by Nonnegative Matrix Factorization. Nature Sayı:401, ss:788–791.
  • [19] Vasiloglou, N., Gray, A.G., and Anderson, D.V., (2009). Non-Negative Matrix Factorization, Convexity and Isometry. Computer Science, Artificial Intelligence.

MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI

Year 2019, Volume: 14 Issue: 1, 26 - 38, 31.01.2019

Abstract

Kör kaynak ayrıştırma, birden fazla sinyalin
karışımını içeren bir veri kümesinden bu karışımı oluşturan her bir kaynağın
tahmin edilmesi olarak tanımlanabilir. Bu işlemin kör olarak adlandırılması
kaynaklar hakkında hiçbir ek bilgi olmadığını belirtmektedir.
 Kör
kaynak ayrıştırma da tahmin edilecek sinyal sayısı kadar karışım sinyali
varken, tek kanal kör kaynak ayrıştırma işleminde sadece bir karışım sinyali
olduğundan kaynakların tahmini maliyetli bir işlemdir. Bahsedilen bu yöntemle
herhangi bir işarete eklenmiş istenmeyen bir gürültü kaynak işaretinden
ayrıştırılabilir. Benzer şekilde bu çalışmada olduğu gibi tek bir mikrofonla
kaydedilmiş 2 farklı enstrümantal işaret kaynağı birbirinden ayrıştırılabilir.
Yapılan bu çalışmada sürekli dalgacık dönüşümü kullanılarak negatif olmayan
matris ayrıştırma ile tek kanallı olarak kaydedilen iki işaret birbirinden
ayrıştırılmıştır. Önerilen yöntemin başarım analizini değerlendirmek için sonuçlar
işaret gürültü oranı ve işaret bozulma oranı cinsinden
değerlendirilmiştir. 

References

  • [1] Niknazar, M., (2014). Blind Source Separation of Underdetermined Mixtures Of Event-Related Sources. Signal Processessing: Cilt:101, Sayı:8, ss:52–64.
  • [2] Nikunen, J.T. and Virtanen, T., (2014). Direction of Arrival Based Spatial Covariance Model for Blind Sound Source Separation IEEE/ACM Trans. Audio Speech Lang. Process., Cilt:22, Sayı:3, ss:727-739.
  • [3] Pengju, H., Tingting, S., Wenhui, L., and Weibiao, Y., (2018). Single Channel Blind Source Separation on The Instantaneous Mixed Signal Of Multiple Dynamic Sources. Mechanical Systems and Signal Processing. Cilt:113, ss:22-35.
  • [4] Smaragdis, P., (2004). Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sourses from Monophonic Inputs. Lecture Notes in Computer Science, Cilt:319, Sayı:5, ss:494–499.
  • [5] Schmidt, M.N. and Olsson, R.K., (2006). Singlechannel Speech Separation Using Sparse Non-negative Matrix Factorization, International Conference on Spoken Language Processing (INTERSPEECH), Glasgow.
  • [6] Kırbız, S. ve Günsel, B., (2009). Negatif Olmayan Matris Ayrıştırma ile Tek-Kanaldan Algısal Ses Ayrıştırma, IEEE, Antalya.
  • [7] He, P., She, T., Li, W., and Yuan, W., (2018). Single Channel Blind Source Separation on The Instantaneous Mixed Signal of Multiple Dynamic Sources. Mechanical Systems and Signal Processing. Sayı:113, ss: 22-35.
  • [8] Schmidt, M.N., (2008). Single-Channel Source Separation Using Non-negative Matrix Factorization, Doktora Tezi, Technical University of Denmark.
  • [9] Mateo, C. and Talavera, J.A., (2018). Short-time Fourier transform with the window size fixed in the frequency domain. Digital Signal Processing. Cilt:77, ss:13-21.
  • [10] Mateo, C. and Talavera, J.A., (2018). Short-Time Fourier Transform with the Window Size Fixed in the Frequency Domain (STFT-FD): Implementation. SoftwareX, Cilt:8, ss:5-8.
  • [11] Choukri, D. and Mehdi, A., (2017). A New Data-Driven Deep Learning Model for Pattern Categorization using Fast Independent Component Analysis and Radial Basis Function Network. Taking Social Networks resources as a case Procedia Computer Science, Cilt:113, ss:97-104.
  • [12] Vanluyten, B., Jan, C., and Bart De Moor, B.M., (2008). Structured Nonnegative Matrix Factorization with Applications to Hidden Markov Realization and Clustering. Linear Algebra and its Applications, Cilt:429, Sayı:7, ss:1409-1424.
  • [13] Cardoso, J.F. and Comon, P., (1996). Independent Component Analysis, A Survey of Some Algebraic Methods, Proc. ISCAS96, ss:93-96.
  • [14] Comon, P., (1994). Independent Component Analysis: A New Concept, Signal Processing, 36, 287-314.
  • [15] O’Grady, P.D., Pearlmutter, B.A., and Rickard, S.T., (2005). Survey of Sparse and Non-sparse Methods in Source Separation, IJIST International Journal of Imaging Systems and Technology, 18, ss: 78-85.
  • [16] Kanailal Mahato K., (2018). the composition of fractional hankel wavelet transform on some function spaces. Applied Mathematics and Computation, Cilt:337, ss:76-86.
  • [17] Dai, H., Zheng, Z., Wang, W., (2018). A new ractional wavelet transform. Communications in Nonlinear Science and Numerical Simulation, Sayı:44, ss:19-36.
  • [18] Lee, D. and Seung, H., (1999). Learning the Parts of Objects by Nonnegative Matrix Factorization. Nature Sayı:401, ss:788–791.
  • [19] Vasiloglou, N., Gray, A.G., and Anderson, D.V., (2009). Non-Negative Matrix Factorization, Convexity and Isometry. Computer Science, Artificial Intelligence.
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Hüsamettin Çelik 0000-0002-7662-0674

Fatih Yavuz Ilgın 0000-0002-7449-4811

Yusuf Sevim This is me 0000-0001-9649-2465

Publication Date January 31, 2019
Published in Issue Year 2019 Volume: 14 Issue: 1

Cite

APA Çelik, H., Ilgın, F. Y., & Sevim, Y. (2019). MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI. Engineering Sciences, 14(1), 26-38.
AMA Çelik H, Ilgın FY, Sevim Y. MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI. Engineering Sciences. January 2019;14(1):26-38.
Chicago Çelik, Hüsamettin, Fatih Yavuz Ilgın, and Yusuf Sevim. “MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI”. Engineering Sciences 14, no. 1 (January 2019): 26-38.
EndNote Çelik H, Ilgın FY, Sevim Y (January 1, 2019) MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI. Engineering Sciences 14 1 26–38.
IEEE H. Çelik, F. Y. Ilgın, and Y. Sevim, “MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI”, Engineering Sciences, vol. 14, no. 1, pp. 26–38, 2019.
ISNAD Çelik, Hüsamettin et al. “MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI”. Engineering Sciences 14/1 (January 2019), 26-38.
JAMA Çelik H, Ilgın FY, Sevim Y. MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI. Engineering Sciences. 2019;14:26–38.
MLA Çelik, Hüsamettin et al. “MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI”. Engineering Sciences, vol. 14, no. 1, 2019, pp. 26-38.
Vancouver Çelik H, Ilgın FY, Sevim Y. MÜZİK İŞARETLERİN TEK KANAL KÖR KAYNAK AYRIŞTIRMA İLE AYRIŞTIRILMASI. Engineering Sciences. 2019;14(1):26-38.