Identification of Electronic Disguised Voices

Wu, Haojun; Wang, Yong; Huang, Jiwu

doi:10.1109/tifs.2014.2301912

Cited by 43 publications

(26 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the frequency of speech signals is not linear, it requires the study of a new frequency unit that effectively represents the relationship between the size and the frequency. It must also satisfy the following three conditions: the general physical linear description, a low level of discrimination in the high frequency range, and a high level of discrimination in the low frequency range [41,43,[46][47][48][49][50][51].…”

Section: Novel and Reasonableness Hypothesis For The Accurately-solvementioning

confidence: 99%

See 1 more Smart Citation

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Ren

Chai

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).

show abstract

Section: Novel and Reasonableness Hypothesis For The Accurately-solvementioning

confidence: 99%

“…The EEG signals can be set with a 23.6 s duration to guarantee short-time stationarity inside a frame. Generally, there is an overlap with a duration of one-half of a frame between two adjacent frames [46][47][48]50,51]. The hamming window was applied to each frame to obtain windowed frames, and can be calculated as follows:…”

mentioning

confidence: 99%

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Ren

Chai

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In addition,voice recognition could automatically and accurately extract voice signatures from a speakers voice and even could automatically recognize fury in audio records [73] as well as help identify electronic disguised voices [74] E. Medical Diagnosis SVM can help doctors diagnose some diseases like vocal disorders [75] and intestinal ischemia [76].At the same time,classification of data [77] and making medical decision [78] are another two important applications.…”

Section: Voice Recognitionmentioning

confidence: 99%

Introduction of SVM algorithms and recent applications about fault diagnosis and other aspects

Yin

Liu

Krueger

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

View full text Add to dashboard Cite

Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory.At the same time,there are increasing concerns about the fault diagnosis for practical engineering systems.Firstly,many kinds of SVM algorithms will be introduced,such as LS-SVM,LSVM and PSVM and so on.Besides,the advantages and disadvantage of those methods will be introduced.Finally,we discuss fault diagnosis and other aspects' recent applications and the directions we should research in the future.

show abstract

“…The researches on de-identification detection are relatively fewer. Paper [36,37,38] proposed detection algorithms using MFCC features and SVM. The cross-database recognition rates were lower than 90%, and the computation load was heavy.…”

Section: Introductionmentioning

confidence: 99%

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

Wang

Zhu

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Nowadays, speaker disguise is a common operation that presents a great challenge to social security. Therefore, it is important to recognize the authenticity of speech. Most of the current researches focus on speech spoofing, which simulates a target speaker to break through the state-of-art ASV systems by increasing false acceptance rate. Meanwhile, there is another type of disguise, i.e. de-identification, which transforms a speech signal without a target to increase the false rejection rate in order not to be recognized. It has received far less attention. Therefore, in this paper, we investigate the de-identification model and propose a method to detect de-identification speeches from genuine speeches by using a very deep dense convolutional network with 135 layers. The experimental results show that the average accuracy of the proposed method outperforms the reported state-of-the-art methods.

show abstract

Identification of Electronic Disguised Voices

Cited by 43 publications

References 12 publications

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Introduction of SVM algorithms and recent applications about fault diagnosis and other aspects

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

Contact Info

Product

Resources

About