Musical pitch estimation using a supervised single hidden layer feed-forward neural network

@article{Taweewat2013MusicalPE,
  title={Musical pitch estimation using a supervised single hidden layer feed-forward neural network},
  author={Pat Taweewat and Chai Wutiwiwatchai},
  journal={Expert Syst. Appl.},
  year={2013},
  volume={40},
  pages={575-589}
}
Musical pitch estimation is used to find musical note pitch or the fundamental frequency (F0) of audio signal which can be applied to a pre-processing part of many applications such as sound separation, musical note transcription, etc. In this work, a method for the pitch estimation based on classification framework has been designed using a supervised single hidden layer feed-forward neural network. To make this method have good performances in terms of generalization, high-speed training and… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 38 references

A computationally efficient multipitch analysis model

IEEE Trans. Speech and Audio Processing • 2000
View 3 Excerpts
Highly Influenced

Pitch detection with a neural-net classifier

IEEE Trans. Signal Processing • 1991
View 5 Excerpts
Highly Influenced

Extreme Learning Machines

IEEE Intelligent Systems • 2013

Gain-robust multi-pitch tracking using sparse nonnegative matrix factorization

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2011

Improved cuckoo search algorithm for feedforward neural network training

E Valian
International Journal of Artificial Intelligence & Applications, • 2011

Model of a neuron trained to extract periodicity

D. Y. Grigorev, N. G. Bibikov
Acoustical Physics 2010, • 2010
View 1 Excerpt

Multi-pitch determination algorithm based on mixture laplacian distribution

2010 International Conference on Audio, Language and Image Processing • 2010

Multilayer perceptrons

E. Alpaydin
In Introduction to Machine Learning, • 2010
View 1 Excerpt

Similar Papers

Loading similar papers…