Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Jahanshah Kabudian"'
Publikováno v:
Iranian Journal of Chemistry & Chemical Engineering, Vol 25, Iss 4, Pp 1-7 (2006)
The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory sig
Externí odkaz:
https://doaj.org/article/e8c04034d68a4e3eaea5bef481053f41
Publikováno v:
IEEE Access. 10:94403-94416
Publikováno v:
Circuits, Systems, and Signal Processing. 40:3996-4017
Speaker adaptation is implemented in order to shift the speaker-independent model closer to the new speaker speech characteristics to improve the speech recognition performance. The kernel eigenspace-based speaker adaptation methods provide satisfact
Publikováno v:
2022 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).
Publikováno v:
2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS).
Publikováno v:
2021 11th International Conference on Computer Engineering and Knowledge (ICCKE).
Publikováno v:
Multimedia Tools and Applications. 79:1261-1289
In recent years, Speech Emotion Recognition (SER) has received considerable attention in affective computing field. In this paper, an improved system for SER is proposed. In the feature extraction step, a hybrid high-dimensional rich feature vector i
Autor:
Shaghayegh Reza, Jahanshah Kabudian
Publikováno v:
Signal and Data Processing. 14:111-134
Autor:
Seyed Jahanshah Kabudian, Ziba Imani
Publikováno v:
2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI).
Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been pr
Publikováno v:
2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS).
In speech signal representation models, autoregressive moving average (ARMA) modeling is used in various applications, such as feature extraction, signal coding, speech synthesis, and speech recognition. In this paper, a new method based on quantum-b