Algorithm for the Complex Discrete Cosine Transform

Popov, Oliver; Chernysheva, T. V.; Orlov, K. V.; Sapronov, P. S.

doi:10.1109/tirved56496.2022.9965537

Cited by 3 publications

(2 citation statements)

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“…The significance of this property lies in the potential concentration of speech signal energy into few coefficients. In scenarios where the bulk of energy is channeled into a limited number of coefficients, a succinct set of features would aptly capture the distinct attributes of speakers [ 18 , 19 ]. where N is the number of subcarriers, 0≤ n ≤ N −1, and …”

Section: Feature Extraction Stagesmentioning

confidence: 99%

“…In the realm of speaker identification systems, discrete transform domains can give more representative MFCCs. This section delves into the exploration of three pivotal discrete transforms; the Discrete Cosine Transform (DCT), the Discrete Sine Transform (DST), and the Discrete Wavelet Transform (DWT) [18][19][20][21]. All of which hold potential for robust MFCC extraction.…”

Section: Utilization Of Discrete Transformsmentioning

confidence: 99%

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Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Hassan,

Neyazi,

Seddeq

et al. 2024

PLoS ONE

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This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.

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