A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Esfandian N.; Hosseinpour K.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Engineering Year:2021 | Volume:34 | Issue:2 Page(s): 452-457

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

Author(s)

Esfandian N. | Hosseinpour K. | Issue Writer Certificate

Keywords

Spectro-temporal Features

Auditory Model

Feature Extraction

Clustering

Artificial Neural Network

Abstract

In this paper, a new Feature Extraction method is presented based on spectro-temporal representation of speech signal for phoneme classification. In the proposed method, an Artificial Neural Network approach is used to cluster spectro-temporal domain. Self-organizing map Artificial Neural Network (SOM) was applied to Clustering of features space. Scale, rate and frequency were used as spatial information of each point and the magnitude component was used as similarity attribute in Clustering algorithm. Three mechanisms were considered to select attributes in Spectro-temporal Features space. Spatial information of clusters, the magnitude component of samples in spectro-temporal domain and the average of the amplitude components of each cluster points were considered as secondary features. The proposed features vectors were used for phonemes classification. The results demonstrate that a significant improvement is obtained in classification rate of different sets of phonemes in comparison to previous Clustering-based methods. The obtained results of new features indicate the system error is compensated in all vowels and consonants subsets in compare to weighted K-means Clustering.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans