Feature extraction with mel scale separation method on noise audio recordings

TL;DR

This study enhances noise audio feature extraction by introducing a dual-channel MFCC method that improves accuracy over traditional single-channel approaches, especially in noisy conditions.

Contribution

The paper proposes a novel dual-channel MFCC feature extraction method that improves noise robustness compared to standard single-channel techniques.

Findings

Dual-channel MFCC outperforms single-channel in noisy environments.

Accuracy increases from 47.5% to 76.25% with dual-channel in -16 dB noise.

High-quality recordings achieve over 97% accuracy with dual-channel MFCC.

Abstract

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.