An Efficient K-Means Clustering Initialization Using Optimization Algorithm

Divya, V.; Deepika, R.; Yamini, C.; Sobiyaa, P.

doi:10.1109/icacce46606.2019.9079998

Cited by 1 publication

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature analysis on each channel uses k-mean clustering, the data for each feature in the word sample is calculated for the cluster center [23], [28]. Identification determines whether a sample of words is identical or not by comparing the distance to the center of the cluster.…”

Section: K-mean Clusteringmentioning

confidence: 99%

See 1 more Smart Citation

Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

View full text Add to dashboard Cite

This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produces high accuracy. While the low-quality is because of noise, the accuracy is low. Improved accuracy by investigating the effect of bandwidth on the mel scale. The proposed improvement uses the mel scale separation methods into two frequency channels (MFCC dual-channel). For the comparison method using the mel scale bandwidth without separation (MFCC single-channel). Feature analysis using k-mean clustering. The data uses a noise variance of up to -16 dB. Testing on the MFCC single-channel method for -16 dB noise has an accuracy of 47.5%, while the MFCC dual-channel method has an accuracy better of 76.25%. The next test used adaptive noise-canceling (ANC) to reduce noise before extraction. The result is that the MFCC single-channel method has an accuracy of 82.5% and the MFCC dual-channel method has an accuracy better of 83.75%. High-quality audio recording testing for the MFCC single-channel method has an accuracy of 92.5% and the MFCC dual-channel method has an accuracy better of 97.5%. The test results show the effect of mel scale bandwidth to increase accuracy. The MFCC dual-channel method has higher accuracy.

show abstract

Section: K-mean Clusteringmentioning

confidence: 99%

“…Minimum MSE means that the noise in the word sample is reduced [23]. The noise reduction is determined by the accuracy of the weight value on the adaptive linear combiner.…”

mentioning

confidence: 99%

Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

View full text Add to dashboard Cite

show abstract

An Efficient K-Means Clustering Initialization Using Optimization Algorithm

Cited by 1 publication

References 5 publications

Feature extraction with mel scale separation method on noise audio recordings

Feature extraction with mel scale separation method on noise audio recordings

Contact Info

Product

Resources

About