Mitigating effects of noise in Forensic Speaker Recognition

Athulya, M. S.; Vinayshankar,; Sathidevi, P. S.

doi:10.1109/wispnet.2017.8300031

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…To determine the performance of the MFCC dual-channel and MFCC single-channel methods by calculating the values of true positive (TP), true negative (TN), false positive (FP), false negative (FN) [7]. TP is a sample of words stating true, and the test results are identical.…”

Section: 𝐷(𝑥mentioning

confidence: 99%

“…Audio tapping by the authorities is an activity to record without being noticed. Wiretapping recordings are not necessarily good quality, because they can't choose the environmental situation when the tapping takes place [6], [7]. Noisy environment, wiretapping recording is noise, the audio recording quality is low [7].…”

Section: Introductionmentioning

confidence: 99%

“…Wiretapping recordings are not necessarily good quality, because they can't choose the environmental situation when the tapping takes place [6], [7]. Noisy environment, wiretapping recording is noise, the audio recording quality is low [7]. This affects the accuracy of the information contained in the recording [5], [8].…”

Section: Introductionmentioning

confidence: 99%

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Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

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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.

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Section: 𝐷(𝑥mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

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“…Human-computer interaction increasingly relies on Machine Learning (ML) models such as Deep Neural Networks (DNNs) trained from, usually large, datasets [1,2,3,4]. The ubiquitous applications of DNNs in security-critical tasks, such as face identity recognition [5,6], speaker verification [7,8], voice controlled systems [9,10,11] or signal forensics [12,13,14,15] require a high reliability on these computational models. However, it has been demonstrated that such models can be fooled by perturbing an input sample with malicious and quasi-imperceptible perturbations.…”

Section: Introductionmentioning

confidence: 99%

On the human evaluation of audio adversarial examples

Vadillo¹,

Santana²

2020

Preprint

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Human-machine interaction is increasingly dependent on speech communication. Machine Learning models are usually applied to interpret human speech commands. However, these models can be fooled by adversarial examples, which are inputs intentionally perturbed to produce a wrong prediction without being noticed. While much research has been focused on developing new techniques to generate adversarial perturbations, less attention has been given to aspects that determine whether and how the perturbations are noticed by humans. This question is relevant since high fooling rates of proposed adversarial perturbation strategies are only valuable if the perturbations are not detectable. In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable measure of the human perception of the perturbations. Using an analytical framework, and an experiment in which 18 subjects evaluate audio adversarial examples, we demonstrate that the metrics employed by convention are not a reliable measure of the perceptual similarity of adversarial examples in the audio domain.

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“…Forensic speaker recognition in noise for mitigating effects is discussed in [8]. In preprocessing stage voice activity detector is used to achieve the robustness.…”

Section: Introductionmentioning

confidence: 99%

Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models

Sajeer*,

Rodrigues

2019

IJEAT

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Identification of a person’s voice from the different voices is known as speaker recognition. The speech signals of individuals are selected by means of speaker recognition or identification. In this work, an efficient method for speaker recognition is made by using Discrete Wavelet Transform (DWT) features and Gaussian Mixture Models (GMM) for classification is presented. The input speech signal features are decomposed by DWT into subband coefficients. The DWT subband coefficient features are the input for the classification. Classification is made by GMM classifier at 4, 8, 16 and 32 Gaussian component levels. Results show a better accuracy of 96.18% speaker signals using DWT features and GMM classifier

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Mitigating effects of noise in Forensic Speaker Recognition

Cited by 5 publications

References 18 publications

Feature extraction with mel scale separation method on noise audio recordings

Feature extraction with mel scale separation method on noise audio recordings

On the human evaluation of audio adversarial examples

Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models

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