Classification of Recurrence Plots of Voice Signals Using Convolutional Neural Networks

Barros, L. De; Soares, Gabriel; Correia, Suzete; Duarte, Gabriel Daltro; Costa, Silvana Cunha

doi:10.14209/sbrt.2020.1570661665

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…However, these nonlinear features cannot be used as a stand-alone feature to achieve a performance that is anywhere close to what state-of-the-art techniques (using the linear feature) achieve. These recent techniques are based on deep learning architectures such as Convolutional Neural Networks (CNN) 24 , Wav2Vec2.0 25 , Deeper Feature CNN-Connectionist Temporal Classification (DFCNN-CTC) 26 , Gated Recurrence Unit-CNN (GRU-CNN) 27 etc.…”

Section: Nonlinear Dynamics Of Vocal Tract For Speaker Modelingmentioning

confidence: 99%

Recurrence plot embeddings as short segment nonlinear features for multimodal speaker identification using air, bone and throat microphones

Nawas,

Shahina,

Balachandar

et al. 2024

Sci Rep

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Speech is produced by a nonlinear, dynamical Vocal Tract (VT) system, and is transmitted through multiple (air, bone and skin conduction) modes, as captured by the air, bone and throat microphones respectively. Speaker specific characteristics that capture this nonlinearity are rarely used as stand-alone features for speaker modeling, and at best have been used in tandem with well known linear spectral features to produce tangible results. This paper proposes Recurrent Plot (RP) embeddings as stand-alone, non-linear speaker-discriminating features. Two datasets, the continuous multimodal TIMIT speech corpus and the consonant-vowel unimodal syllable dataset, are used in this study for conducting closed-set speaker identification experiments. Experiments with unimodal speaker recognition systems show that RP embeddings capture the nonlinear dynamics of the VT system which are unique to every speaker, in all the modes of speech. The Air (A), Bone (B) and Throat (T) microphone systems, trained purely on RP embeddings perform with an accuracy of 95.81%, 98.18% and 99.74%, respectively. Experiments using the joint feature space of combined RP embeddings for bimodal (A–T, A–B, B–T) and trimodal (A–B–T) systems show that the best trimodal system (99.84% accuracy) performs on par with trimodal systems using spectrogram (99.45%) and MFCC (99.98%). The 98.84% performance of the B–T bimodal system shows the efficacy of a speaker recognition system based entirely on alternate (bone and throat) speech, in the absence of the standard (air) speech. The results underscore the significance of the RP embedding, as a nonlinear feature representation of the dynamical VT system that can act independently for speaker recognition. It is envisaged that speech recognition too will benefit from this nonlinear feature.

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Section: Nonlinear Dynamics Of Vocal Tract For Speaker Modelingmentioning

confidence: 99%

Recurrence plot embeddings as short segment nonlinear features for multimodal speaker identification using air, bone and throat microphones

Nawas,

Shahina,

Balachandar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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Exploring Price Patterns of Vegetables with Recurrence Quantification Analysis

Karakasidou,

Fragkou,

Zachilas

et al. 2024

AppliedMath

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This study investigates the time-series behavior of vegetable prices in the Central Market of Thessaloniki, Greece, using Recurrence Plot (RP) analysis and Recurrence Quantification Analysis (RQA), which considers non-linearities and does not necessitate stationarity of time series. The period of study was 1999–2016 for practical and research reasons. In the present work, we focus on vegetables available throughout the year, exploring the dynamics and interrelationships between their prices to avoid missing data. The study applies RP visual inspection classification, a clustering based on RQA parameters, and a classification based on the RQA analysis graphs with epochs for the first time. The aim of the paper was to investigate the grouping of products based on their price dynamical behavior. The results show that the formed groups present similarities related to their use as dishes and their way of cultivation, which apparently affect the price dynamics. The results offer insights into market behaviors, helping to inform better management strategies and policymaking and offer a possibility to predict variability of prices. This information can interest government policies in various directions, such as what products to develop for greater stability, identity for fluctuating prices, etc. In future work, a larger dataset including missing data could be included, as well as a machine-learning algorithm to classify the products based on the RQA with epochs graphs.

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Classification of Recurrence Plots of Voice Signals Using Convolutional Neural Networks

Cited by 2 publications

References 7 publications

Recurrence plot embeddings as short segment nonlinear features for multimodal speaker identification using air, bone and throat microphones

Recurrence plot embeddings as short segment nonlinear features for multimodal speaker identification using air, bone and throat microphones

Exploring Price Patterns of Vegetables with Recurrence Quantification Analysis

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