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Добірка наукової літератури з теми "VOICE SIGNALS"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "VOICE SIGNALS"

Ahamed, Mohamed Rasmi Ashfaq, Mohammad Hossein Babini, and Hamidreza Namazi. "Complexity-based decoding of the relation between human voice and brain activity." Technology and Health Care 28, no. 6 (November 17, 2020): 665–74. http://dx.doi.org/10.3233/thc-192105.

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BACKGROUND: The human voice is the main feature of human communication. It is known that the brain controls the human voice. Therefore, there should be a relation between the characteristics of voice and brain activity. OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed. METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed. RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours. CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.

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Mittal, Vikas, and R. K. Sharma. "Classification of Pathological Voices Using Glottal Signal Parameters." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 2019): 3999–4002. http://dx.doi.org/10.1166/jctn.2019.8284.

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The discrimination of voice signals has numerous applications in diagnosing of pathologies related to voice. This paper discussed about the glottal signal that is bound to recognize two sorts of voice issue: Laryngitis and Laryngeal dystonia (LD). The parameters of the glottal signal fill in as contribution to classifiers that characterizes into three unique gatherings of speakers: speakers with Laryngitis; with laryngeal dystonia (LD); lastly speakers with healthy voices. The database is made out of voice accounts containing tests of three gatherings. The classifiers SVM provided 60%, KNN provided 70% and Ensemble provided 80% classification accuracy in the case of Laryngitis. Voice signals of patients affected with Laryngeal dystonia were also collected and tested with same classifiers and the Accuracy of 90%, 80% and 50% were obtained with SVM, KNN and Ensemble respectively.

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Silva, Augusto Felix Tavares, Samuel R. de Abreu, Silvana Cunha Costa, and Suzete Elida Nobrega Correia. "Classificação de sinais de voz através da aplicação da transformada Wavelet Packet e redes neurais artificiais." Revista Principia - Divulgação Científica e Tecnológica do IFPB 1, no. 37 (December 21, 2017): 34. http://dx.doi.org/10.18265/1517-03062015v1n37p34-41.

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Pathologies such as edema, nodules and paralysis are quite recurrent and directly influence vocal dysfunctions. The acoustic analysis has been used to evaluate the disorders caused in the voice signals, detecting the presence of pathologies in the larynx, through digital signal processing techniques. This work aims to distinguish healthy voice signals from the ones affected by laryngeal pathologies, using the Wavelet Packet transform in the feature extraction step. Energy and entropy measures, in six resolution levels, obtained through the Daubechies wavelet of order 4 are used in the discrimination of the voice signals. The classification is done through Artificial Neural Networks. Accuracies above 90% were obtained, with the entropy measure, in the discrimination between healthy voices and affected ones by pathologies in the vocal folds (nodules, Reinke’s edema and paralysis)

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Choi, Hee-Jin, and Ji-Yeoun Lee. "Comparative Study between Healthy Young and Elderly Subjects: Higher-Order Statistical Parameters as Indices of Vocal Aging and Sex." Applied Sciences 11, no. 15 (July 28, 2021): 6966. http://dx.doi.org/10.3390/app11156966.

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The objective of this study was to test higher-order statistical (HOS) parameters for the classification of young and elderly voice signals and identify gender- and age-related differences through HOS analysis. This study was based on data from 116 subjects (58 females and 58 males) extracted from the Saarbruecken voice database. In the gender analysis, the same number of voice samples were analyzed for each sex. Further, we conducted experiments on the voices of elderly people using gender analysis. Finally, we reviewed the standards and reference models to reduce sex and gender bias. The acoustic parameters were extracted from young and elderly voice signals using Praat and a time–frequency analysis program (TF32). Additionally, we investigated the gender- and age-related differences in HOS parameters. Young and elderly voice signals significantly differed in normalized skewness (p = 0.005) in women and normalized kurtosis (p = 0.011) in men. Therefore, normalized skewness is a useful parameter for distinguishing between young and elderly female voices, and normalized kurtosis is essential for distinguishing between young and elderly male voices. We will continue to investigate parameters that represent important information in elderly voice signals.

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Swanborough, Huw, Matthias Staib, and Sascha Frühholz. "Neurocognitive dynamics of near-threshold voice signal detection and affective voice evaluation." Science Advances 6, no. 50 (December 2020): eabb3884. http://dx.doi.org/10.1126/sciadv.abb3884.

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Communication and voice signal detection in noisy environments are universal tasks for many species. The fundamental problem of detecting voice signals in noise (VIN) is underinvestigated especially in its temporal dynamic properties. We investigated VIN as a dynamic signal-to-noise ratio (SNR) problem to determine the neurocognitive dynamics of subthreshold evidence accrual and near-threshold voice signal detection. Experiment 1 showed that dynamic VIN, including a varying SNR and subthreshold sensory evidence accrual, is superior to similar conditions with nondynamic SNRs or with acoustically matched sounds. Furthermore, voice signals with affective meaning have a detection advantage during VIN. Experiment 2 demonstrated that VIN is driven by an effective neural integration in an auditory cortical-limbic network at and beyond the near-threshold detection point, which is preceded by activity in subcortical auditory nuclei. This demonstrates the superior recognition advantage of communication signals in dynamic noise contexts, especially when carrying socio-affective meaning.

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Liu, Boquan, Evan Polce, and Jack Jiang. "Application of Local Intrinsic Dimension for Acoustical Analysis of Voice Signal Components." Annals of Otology, Rhinology & Laryngology 127, no. 9 (June 17, 2018): 588–97. http://dx.doi.org/10.1177/0003489418780439.

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Purpose: The overall aim of this study was to apply local intrinsic dimension ( Di) estimation to quantify high-dimensional, disordered voice and discriminate between the 4 types of voice signals. It was predicted that continuous Di analysis throughout the entire time-series would generate comprehensive descriptions of voice signal components, called voice type component profiles (VTCP), that effectively distinguish between the 4 voice types. Method: One hundred thirty-five voice recording samples of the sustained vowel /a/ were obtained from the Disordered Voice Database Model 4337 and spectrographically classified into the voice type paradigm. The Di and correlation dimension ( D2) were then used to objectively analyze the voice samples and compared based on voice type differentiation efficacy. Results: The D2 exhibited limited effectiveness in distinguishing between the 4 voice type signals. For Di analysis, significant differences were primarily observed when comparing voice type component 1 (VTC1) and 4 (VTC4) across the 4 voice type signals ( P < .001). The 4 voice type components (VTCs) significantly differentiated between low-dimensional, type 3 and high-dimensional, type 4 signals ( P < .001). Conclusions: The Di demonstrated improvements over D2 in 2 distinct manners: enhanced resolution at high data dimensions and comprehensive description of voice signal elements.

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Martin, David P., and Virginia I. Wolfe. "Effects of Perceptual Training Based upon Synthesized Voice Signals." Perceptual and Motor Skills 83, no. 3_suppl (December 1996): 1291–98. http://dx.doi.org/10.2466/pms.1996.83.3f.1291.

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28 undergraduate students participated in a perceptual voice experiment to assess the effects of training utilizing synthesized voice signals. An instructional strategy based upon synthesized examples of a three-part classification system: “breathy,” “rough,” and “hoarse,” was employed. Training samples were synthesized with varying amounts of jitter (cycle-to-cycle deviation in pitch period) and harmonic-to-noise ratios to represent these qualities. Before training, listeners categorized 60 pathological voices into “breathy,” “rough,” and “hoarse,” largely on the basis of fundamental frequency. After training, categorizations were influenced by harmonic-to-noise ratios as well as fundamental frequency, suggesting that listeners were more aware of spectral differences in pathological voices associated with commonly occurring laryngeal conditions. 40% of the pathological voice samples remained unclassified following training.

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Zhu, Xin-Cheng, Deng-Huang Zhao, Yi-Hua Zhang, Xiao-Jun Zhang, and Zhi Tao. "Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection." Applied Sciences 12, no. 18 (September 14, 2022): 9196. http://dx.doi.org/10.3390/app12189196.

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Due to the complexity and non-stationarity of the voice generation system, the nonlinearity of speech signals cannot be accurately quantified. Recently, the recurrence quantification analysis method has been used for voice disorder detection. In this paper, multiscale recurrence quantification measures (MRQMs) are proposed. The signals are reconstructed in the high-dimensional phase space at the equivalent rectangular bandwidth scale. Recurrence plots (RPs) combining the characteristics of human auditory perception are drawn with an appropriate recurrence threshold. Based on the above, the nonlinear dynamic recurrence features of the speech signal are quantized from the recurrence plot of each frequency channel. Furthermore, this paper explores the recurrence quantification thresholds that are most suitable for pathological voices. Our results show that the proposed MRQMs with support vector machine (SVM), random forest (RF), Bayesian network (BN) and Local Weighted Learning (LWL) achieve an average accuracy of 99.45%, outperforming traditional features and other complex measurements. In addition, MRQMs also have the potential for multi-classification of voice disorder, achieving an accuracy of 89.05%. This study demonstrates that MRQMs can characterize the recurrence characteristic of pathological voices and effectively detect voice disorders.

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Bartusiak, Emily R., and Edward J. Delp. "Frequency Domain-Based Detection of Generated Audio." Electronic Imaging 2021, no. 4 (January 18, 2021): 273–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-273.

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Attackers may manipulate audio with the intent of presenting falsified reports, changing an opinion of a public figure, and winning influence and power. The prevalence of inauthentic multimedia continues to rise, so it is imperative to develop a set of tools that determines the legitimacy of media. We present a method that analyzes audio signals to determine whether they contain real human voices or fake human voices (i.e., voices generated by neural acoustic and waveform models). Instead of analyzing the audio signals directly, the proposed approach converts the audio signals into spectrogram images displaying frequency, intensity, and temporal content and evaluates them with a Convolutional Neural Network (CNN). Trained on both genuine human voice signals and synthesized voice signals, we show our approach achieves high accuracy on this classification task.

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Liu, Boquan, Evan Polce, Julien C. Sprott, and Jack J. Jiang. "Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods." Journal of Speech, Language, and Hearing Research 61, no. 5 (May 17, 2018): 1130–39. http://dx.doi.org/10.1044/2018_jslhr-s-17-0250.

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Purpose The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression. Study Design Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100 Monte Carlo experiments were applied to analyze the output of jitter, shimmer, correlation dimension, and spectrum convergence ratio. The computational output of the 4 classifiers was then plotted against signal chaos level to investigate the performance of these acoustic analysis methods under varying degrees of signal chaos. Method A diffusive behavior detection–based chaos level test was used to investigate the performances of different voice classification methods. Voice signals were constructed by varying the signal-to-noise ratio to establish differing signal chaos conditions. Results Chaos level increased sigmoidally with increasing noise power. Jitter and shimmer performed optimally when the chaos level was less than or equal to 0.01, whereas correlation dimension was capable of analyzing signals with chaos levels of less than or equal to 0.0179. Spectrum convergence ratio demonstrated proficiency in analyzing voice signals with all chaos levels investigated in this study. Conclusion The results of this study corroborate the performance relationships observed in previous studies and, therefore, demonstrate the validity of the validation test method. The presented chaos level validation test could be broadly utilized to evaluate acoustic analysis methods and establish the most appropriate methodology for objective voice analysis in clinical practice.

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Більше джерел

Дисертації з теми "VOICE SIGNALS"

Wu, Cheng. "A typology for voice and music signals." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27082.

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With the high increase in the availability of digital music, it has become of interest to automatically query a database of musical pieces. At the same time, a feasible solution of this objective gives us an insight into how humans perceive and classify music. In this research, we discuss our approach to classify music into four categories: pop, classical, country and jazz. Songs are collected in wave format. We randomly chose five 10-second clips from different parts of a song. We discussed two families of features: wavelet features and time-based features. These features are capable of capturing the information of energy and time of voice signal. Instead of using traditional Mel-Frequency Cepstral Coefficients (MFCC)[7] methods, which are widely used in audio classification and music classification, we incorporate the features in statistical classification methods such as LDA, QDA and tree. Finally, we attempted to create an adaptive tree approach for classification. In this research, 130 songs are collected. Pop songs are collected in 4 languages, English, Chinese, Spanish and French. A cross validation method is used to compute the proportion of correctly classified songs. It is shown that the tree method has a proportion of correct classification equal 0.80 when pop and country are considered as one category.

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Anskaitis, Aurimas. "Analysis of Quality of Coded Voice Signals." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2009~D_20100303_142141-66509.

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The dissertation investigates the problem of quality of coded voice. The main attention is paid to voice quality evaluation under packet loss conditions. The aim of the work is to improve voice quality evaluation algorithms. The tasks of the work are: • construction of the means for measurement of voice quality of short voice signals; • to define the concept of value of coded voice segment and to choose corresponding value metrics; • to measure distributions of frame values in standard voice; • to establish limits of distortions created by different codecs; • to investigate inertia of wide spread codecs and establish the length of impact of one lost frame. The dissertation consists of the introduction, 4 chapters, conclusions, list of literature. Introduction presents the novelty and topicality of the work, tasks and aims of the work are formulated. The first chapter is overview of voice quality evaluation methods, pros and cons of these methods are analyzed. PESQ algorithm and limits of its applicability are introduced in this chapter too. The lists of Lithuanian words for word intelligibility testing are created. Chapter two presents the method of signal construction that allows to extend PESQ applicability to short signals. This chapter introduces the concept of frame value. Distributions of frame values are calculated. Third chapter analyses distortions created by coding. It is shown that coding distortions... [to full text]
Disertacijoje nagrin jama koduoto balso kokybės vertinimo problematika. Pagrindinis dėmesys skiriamas balso kokybės tyrimams, kai perduodama koduota šneka ir prarandami balso paketai. Darbo tikslas yra patobulinti koduoto balso kokybės vertinimo algoritmus. Darbo uždaviniai yra šie: • sukurti matavimo priemonę trumpų balso signalo atkarpų kokybei vertinti; • apibrėžti koduoto balso segmentų vertės sampratą ir parinkti vertės metrikas; • išmatuoti bendrinės šnekos balso segmentų verčių skirstinius; • nustatyti skirtingų koderių sukuriamų iškraipymų ribas; • ištirti paplitusių koderių inertiškumą, nustatyti kiek laiko pastebima prarastų paketų įtaka sekantiems segmentams. Disertaciją sudaro įvadas, keturi tiriamieji skyriai ir bendrosios išvados. Įvade pristatomas darbo naujumas, aktualumas, aptariamas autoriaus indėlis, formuluojami darbo tikslai. Pirmas skyrius yra apžvalginis – analizuojami balso kokybės vertinimo metodai, jų privalumai ir trūkumai. Kaip savarankiška dalis čia pristatyti autoriaus sudaryti sąrašai lietuviškų žodžių, skirtų šnekos suprantamumo tyrimams. Antrame skyriuje parodoma, kaip galima išplėsti kokybės vertinimo PESQ (angl. Perceptual Evaluation of Speech Quality) algoritmo taikymo ribas. Čia įvedama koduoto balso paketo vertės sąvoka, nustatomi statistiniai paketų vertės skirstiniai. Trečiame skyriuje nagrinėjami specifiniai koduotos šnekos iškraipymai ir kodavimo parametrų įtaka... [toliau žr. visą tekstą]

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Strange, John. "VOICE AUTHENTICATIONA STUDY OF POLYNOMIAL REPRESENTATION OF SPEECH SIGNALS." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4015.

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A subset of speech recognition is the use of speech recognition techniques for voice authentication. Voice authentication is an alternative security application to the other biometric security measures such as the use of fingerprints or iris scans. Voice authentication has advantages over the other biometric measures in that it can be utilized remotely, via a device like a telephone. However, voice authentication has disadvantages in that the authentication system typically requires a large memory and processing time than do fingerprint or iris scanning systems. Also, voice authentication research has yet to provide an authentication system as reliable as the other biometric measures. Most voice recognition systems use Hidden Markov Models (HMMs) as their basic probabilistic framework. Also, most voice recognition systems use a frame based approach to analyze the voice features. An example of research which has been shown to provide more accurate results is the use of a segment based model. The HMMs impose a requirement that each frame has conditional independence from the next. However, at a fixed frame rate, typically 10 ms., the adjacent feature vectors might span the same phonetic segment and often exhibit smooth dynamics and are highly correlated. The relationship between features of different phonetic segments is much weaker. Therefore, the segment based approach makes fewer conditional independence assumptions which are also violated to a lesser degree than for the frame based approach. Thus, the HMMs using segmental based approaches are more accurate. The speech polynomials (feature vectors) used in the segmental model have been shown to be Chebychev polynomials. Use of the properties of these polynomials has made it possible to reduce the computation time for speech recognition systems. Also, representing the spoken word waveform as a Chebychev polynomial allows for the recognition system to easily extract useful and repeatable features from the waveform allowing for a more accurate identification of the speaker. This thesis describes the segmental approach to speech recognition and addresses in detail the use of Chebychev polynomials in the representation of spoken words, specifically in the area of speaker recognition. .
M.S.
Department of Mathematics
Arts and Sciences
Mathematics

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BHATT, HARSHIT. "SPEAKER IDENTIFICATION FROM VOICE SIGNALS USING HYBRID NEURAL NETWORK." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18865.

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Identifying the speaker in audio visual environment is a crucial task which is now surfacing in the research domain researchers nowadays are moving towards utilizing deep neural networks to match people with their respective voices the applications of deep learning are many-fold that include the ability to process huge volume of data robust training of algorithms feasibility of optimization and reduced computation time. Previous studies have explored recurrent and convolutional neural network incorporating GRUs, Bi-GRUs, LSTM, Bi-LSTM and many more[1]. This work proposes a hybrid mechanism which consist of an CNN and LSTM network fused using an early fusion method. We accumulated a dataset of 1,330 voices by recording through a python script of length of 3 seconds in .wav format. The dataset consists of 14 categories and we used 80% for training and 20% for testing. We optimized and fine-tuned the neural networks and modified them to yield optimum results. For the early fusion approach, we used the concatenation operation that fuses neural networks prior to the training phase. The proposed method achieves 97.72% accuracy on our dataset and outperforms all existing baseline mechanisms like MLP, LSTM, CNN, and RNN. This research serves as a contribution to the ongoing research in speaker identification domain and paves way to future directions using deep learning.

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Chandna, Pritish. "Neural networks for singing voice extraction in monaural polyphonic music signals." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673414.

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This thesis dissertation focuses on singing voice extraction from polyphonic musical signals. In particular, we focus on two cases; contemporary popular music, which typically has a processed singing voice with instrumental accompaniment and ensemble choral singing, which involves multiple singers singing in harmony and unison. Over the last decade, several deep learning based models have been proposed to separate the singing voice from instrumental accompaniment in a musical mixture. Most of these models assume that the musical mixture is a linear sum of the individual sources and estimate time-frequency masks to filter out the sources from the input mixture. While this assumption doesn't always hold, deep learning based models have shown remarkable capacity to model the separate sources in a mixture. In this thesis, we propose an alternative method for singing voice extraction. This methodology assumes that the perceived linguistic and melodic content of a singing voice signal is retained even when it is put through a non-linear mixing process. To this end, we explore language independent representations of linguistic content in a voice signal as well as generative methodologies for voice synthesis. Using these, we propose the framework for a methodology to synthesize a clean singing voice signal from the underlying linguistic and melodic content of a processed voice signal in a musical mixture. In addition, we adapt and evaluate state-of-the-art source separation methodologies to separate the soprano, alto, tenor and bass parts of choral recordings. We also use the proposed methodology for extraction via synthesis along with other deep learning based models to analyze unison singing within choral recordings.
Aquesta tesi se centra en l’extracció de veu cantada a partir de senyals musicals polifònics. En particular, ens centrem en dos casos; música popular contemporània, que normalment té una veu cantada processada amb acompanyament instrumental, i cant coral, que consisteix en diversos cantants cantant en harmonia i a l’uníson. Durant l’última dècada, s’han proposat diversos models basats en l’aprenentatge profund per separar la veu de l’acompanyament instrumental en una mescla musical. La majoria d’aquests models assumeixen que la mescla és una suma lineal de les fonts individuals i estimen les màscares temps-freqüència per filtrar les fonts de la mescla d’entrada. Tot i que aquesta assumpció no sempre es compleix, els models basats en l’aprenentatge profund han demostrat una capacitat notable per modelar les fonts en una mescla. En aquesta tesi, proposem un mètode alternatiu per l’extracció de la veu cantada. Aquesta metodologia assumeix que el contingut lingüístic i melòdic que percebem d’un senyal de veu cantada es manté fins i tot quan es tracta d’una mescla no lineal. Per a això, explorem representacions del contingut lingüístic independents de l’idioma en un senyal de veu, així com metodologies generatives per a la síntesi de veu. Utilitzant-les, proposem una metodologia per sintetitzar un senyal de veu cantada a partir del contingut lingüístic i melòdic subjacent d’un senyal de veu processat en una mescla musical. A més, adaptem i avaluem metodologies de separació de fonts d’última generació per separar les parts de soprano, contralt, tenor i baix dels enregistraments corals. També utilitzem la metodologia proposada per a l’extracció mitjançant síntesi juntament amb altres models basats en l’aprenentatge profund per analitzar el cant a l’uníson dins dels enregistraments corals.
Esta disertación doctoral se centra en la extracción de voz cantada a partir de señales musicales polifónicas de audio. En particular, analizamos dos casos; música popular contemporánea, que normalmente contiene voz cantada procesada y acompañada de instrumentación, y canto coral, que involucra a varios coristas cantando en armonía y al unísono. Durante la última década, se han propuesto varios modelos basados en aprendizaje profundo para separar la voz cantada del acompañamiento instrumental en una mezcla musical. La mayoría de estos modelos asumen que la mezcla musical es una suma lineal de fuentes individuales y estiman máscaras de tiempo-frecuencia para extraerlas de la mezcla. Si bien esta suposición no siempre se cumple, los modelos basados en aprendizaje profundo han demostrado tener una gran capacidad para modelar las fuentes de la mezcla. En esta tesis proponemos un método alternativo para extraer voz cantada. Esta técnica asume que el contenido lingüístico y melódico que se percibe en la voz cantada se retiene incluso cuando la señal es sometida a un proceso de mezcla no lineal. Con este fin, exploramos representaciones del contenido lingüístico independientes del lenguaje en la señal de voz, así como metodos generativos para síntesis de voz. Utilizando estas técnicas, proponemos la base para una metodología de síntesis de voz cantada limpia a partir del contenido lingüístico y melódico subyacente de la señal de voz procesada en una mezcla musical. Además, adaptamos y evaluamos metodologías de separación de fuentes de última generación para separar las voces soprano, alto, tenor y bajo de grabaciones corales. También utilizamos la metodología propuesta para extracción mediante síntesis junto con otros modelos basados en aprendizaje profundo para analizar canto al unísono dentro de grabaciones corales.

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Johansson, Dennis. "Real-time analysis, in SuperCollider, of spectral features of electroglottographic signals." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188498.

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This thesis presents tools and components necessary to further develop an implementation of a method. The method attempts to use the non invasive electroglottographic signal to locate rapid transitions between voice registers. Implementations for sample entropy and the Discrete Fourier Transform (DFT) implemented for the programming language SuperCollider are presented along with tools necessary to evaluate the method and present the results in real time. Since different algorithms have been used, both for clustering and cycle separation, a comparison between algorithms for both of these steps has also been done.
Denna rapport presenterar verktyg och komponenter som är nödvändiga för att vidareutveckla en implementation av en metod. Metoden försöker att använda en icke invasiv elektroglottografisk signal för att hitta snabba övergångar mellan röstregister. Det presenteras implementationer för sampelentropi och den diskreta fourier transformen för programspråket SuperCollider samt verktyg som behövs för att utvärdera metoden och presentera resultaten i realtid. Då olika algoritmer har använts för både klustring och cykelseparation så har även en jämförelse mellan algoritmer för dessa steg gjorts.

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Mészáros, Tomáš. "Speech Analysis for Processing of Musical Signals." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234974.

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Hlavním cílem této práce je obohatit hudební signály charakteristikami lidské řeči. Práce zahrnuje tvorbu audioefektu inspirovaného efektem talk-box: analýzu hlasového ústrojí vhodným algoritmem jako je lineární predikce, a aplikaci odhadnutého filtru na hudební audio-signál. Důraz je kladen na dokonalou kvalitu výstupu, malou latenci a nízkou výpočetní náročnost pro použití v reálném čase. Výstupem práce je softwarový plugin využitelný v profesionálních aplikacích pro úpravu audia a při využití vhodné hardwarové platformy také pro živé hraní. Plugin emuluje reálné zařízení typu talk-box a poskytuje podobnou kvalitu výstupu s unikátním zvukem.

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Borowiak, Kamila. "Brain Mechanisms for the Perception of Visual and Auditory Communication Signals – Insights from Autism Spectrum Disorder." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21634.

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Kommunikation ist allgegenwärtig in unserem Alltag. Personen mit einer Autismus-Spektrum-Störung (ASS) zeigen soziale Schwierigkeiten und beim Erkennen von Kommunikationssignalen von Gesicht und Stimme. Da derartige Schwierigkeiten die Lebensqualität beeinträchtigen können, ist ein tiefgreifendes Verständnis der zugrundeliegenden Mechanismen von großer Bedeutung. In der vorliegenden Dissertation befasste ich mich mit sensorischen Gehirnmechanismen, die der Verarbeitung von Kommunikationssignalen zugrunde liegen und, die in der Forschung zu ASS bisher wenig Beachtung fanden. Erstens untersuchte ich, ob eine intranasale Gabe von Oxytocin die Erkennung der Stimmenidentität beeinflussen, und ihre Auffälligkeiten bei Personen mit ASS mildern kann. Zweitens erforschte ich, welche neuronalen Prozesse den Schwierigkeiten in der Wahrnehmung visueller Sprache in ASS zugrunde liegen, da bisherige Evidenz nur auf Verhaltensdaten basierte. Diese Fragestellungen beantwortete ich mit Hilfe von funktioneller Magnetresonanztomographie, Eyetracking und Verhaltenstestungen. Die Ergebnisse der Dissertation liefern neuartige Erkenntnisse, die für Personen mit ASS und typisch entwickelte Personen von hoher Relevanz sind. Erstens bestätigen sie die Annahmen, dass atypische sensorische Mechanismen für unser Verständnis der sozialen Schwierigkeiten in ASS grundlegend sind. Sie zeigen, dass atypische Funktionen sensorischer Gehirnregionen den Kommunikationseinschränkungen in ASS zugrunde liegen und die Effektivität von Interventionen beeinflussen, die jene Schwierigkeiten vermindern sollen. Zweitens liefern die Ergebnisse empirische Evidenz für theoretische Annahmen darüber, wie das typisch entwickelte Gehirn visuelle Kommunikationssignale verarbeitet. Diese Erkenntnisse erweitern maßgeblich unser aktuelles Wissen und zukünftige Forschungsansätze zur zwischenmenschlichen Kommunikation. Außerdem können sie neue Interventionsansätze zur Förderung von Kommunikationsfähigkeiten hervorbringen.
Communication is ubiquitous in our everyday life. Yet, individuals with autism spectrum disorder (ASD) have difficulties in social interactions and to recognize socially relevant signals from the face and the voice. Such impairments can vastly affect the quality of life - a profound understanding of the mechanisms behind these difficulties is thus strongly required. In the current dissertation, I focused on sensory brain mechanisms that underlie the perception of emotionally neutral communication signals that so far have gained little attention in ASD research. I studied the malleability of voice-identity processing using intranasal administration of oxytocin, and thus the potential to alleviate voice-identity recognition impairments in ASD. Furthermore, I investigated brain mechanisms that underlie recognition difficulties for visual speech in ASD, as until now evidence on visual-speech recognition in ASD was limited to behavioral findings. I applied methods of functional magnetic resonance imaging, eye tracking, and behavioral testing. The contribution of the present dissertation is twofold. First, the findings corroborate the view that atypical sensory perception is a critical cornerstone for understanding of social difficulties in ASD. Dysfunction of visual and auditory sensory brain regions might contribute to difficulties in processing aspects of communication signals in ASD and modulate the efficacy of interventions for improving the behavioral deficits. Second, the findings deliver empirical support for a recent theoretical model of how the typically developing brain perceives dynamic faces. This improved our current knowledge about brain processing of visual communication signals in the typically developing population. Advanced scientific knowledge about human communication, as provided in the current dissertation, propels further empirical research and development of clinical interventions that aim to promote communication abilities in affected individuals.

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Mokhtari, Mehdi. "The puzzle of non verbal communication: Towards a new aspect of leadership." Thesis, Linnéuniversitetet, Institutionen för organisation och entreprenörskap (OE), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-26248.

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Communication is surrounding us. Leaders and followers are not an exception to that rule. Indeed, leadership actors are communicating with their co-workers, their boss, their employees, the media, and so forth. However, in the course of this paper and because of its importance, the focus on non verbal communication will be adopted. Basically, this form of communication is everything except the actual words that people pronounce. Body language, tone of the voice, cultural differences, deceit signals, all these components of non verbal communication and many others will be developed. The core of this work will be understanding the main concepts of non verbal communication and then applying them to leaders’ real life situations. This thesis will also, among other things, aim to answer the following questions: What is the importance of non verbal communication in everyday life? How are leaders using non verbal communication to give sense? Do they use deceit signals? What influences the non verbal communication? What is the emotional intelligence concept? Can the non verbal communication be extrapolated and be seen as being inter-cultural?

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Dzhambazov, Georgi. "Knowledge-based probabilistic modeling for tracking lyrics in music audio signals." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/404681.

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This thesis proposes specific signal processing and machine learning methodologies for automatically aligning the lyrics of a song to its corresponding audio recording. The research carried out falls in the broader field of music information retrieval (MIR) and in this respect, we aim at improving some existing state-of-the-art methodologies, by introducing domain-specific knowledge. The goal of this work is to devise models capable of tracking in the music audio signal the sequential aspect of one particular element of lyrics - the phonemes. Music can be understood as comprising different facets, one of which is lyrics. The models we build take into account the complementary context that exists around lyrics, which is any musical facet complementary to lyrics. The facets used in this thesis include the structure of the music composition, structure of a melodic phrase, the structure of a metrical cycle. From this perspective, we analyse not only the low-level acoustic characteristics, representing the timbre of the phonemes, but also higher-level characteristics, in which the complementary context manifests. We propose specific probabilistic models to represent how the transitions between consecutive sung phonemes are conditioned by different facets of complementary context. The complementary context, which we address, unfolds in time according to principles that are particular of a music tradition. To capture these, we created corpora and datasets for two music traditions, which have a rich set of such principles: Ottoman Turkish makam and Beijing opera. The datasets and the corpora comprise different data types: audio recordings, music scores, and metadata. From this perspective, the proposed models can take advantage both of the data and the music-domain knowledge of particular musical styles to improve existing baseline approaches. As a baseline, we choose a phonetic recognizer based on hidden Markov models (HMM): a widely-used methodology for tracking phonemes both in singing and speech processing problems. We present refinements in the typical steps of existing phonetic recognizer approaches, tailored towards the characteristics of the studied music traditions. On top of the refined baseline, we device probabilistic models, based on dynamic Bayesian networks (DBN) that represent the relation of phoneme transitions to its complementary context. Two separate models are built for two granularities of complementary context: the structure of a melodic phrase (higher-level) and the structure of the metrical cycle (finer-level). In one model we exploit the fact the syllable durations depend on their position within a melodic phrase. Information about the melodic phrases is obtained from the score, as well as from music-specific knowledge.Then in another model, we analyse how vocal note onsets, estimated from audio recordings, influence the transitions between consecutive vowels and consonants. We also propose how to detect the time positions of vocal note onsets in melodic phrases by tracking simultaneously the positions in a metrical cycle (i.e. metrical accents). In order to evaluate the potential of the proposed models, we use the lyrics-to-audio alignment as a concrete task. Each model improves the alignment accuracy, compared to the baseline, which is based solely on the acoustics of the phonetic timbre. This validates our hypothesis that knowledge of complementary context is an important stepping stone for computationally tracking lyrics, especially in the challenging case of singing with instrumental accompaniment. The outcomes of this study are not only theoretic methodologies and data, but also specific software tools that have been integrated into Dunya - a suite of tools, built in the context of CompMusic, a project for advancing the computational analysis of the world's music. With this application, we have also shown that the developed methodologies are useful not only for tracking lyrics, but also for other use cases, such as enriched music listening and appreciation, or for educational purposes.
La tesi aquí presentada proposa metodologies d’aprenentatge automàtic i processament de senyal per alinear automàticament el text d’una cançó amb el seu corresponent enregistrament d’àudio. La recerca duta a terme s’engloba en l’ampli camp de l’extracció d’informació musical (Music Information Retrieval o MIR). Dins aquest context la tesi pretén millorar algunes de les metodologies d’última generació del camp introduint coneixement específic de l’àmbit. L’objectiu d’aquest treball és dissenyar models que siguin capaços de detectar en la senyal d’àudio l’aspecte seqüencial d’un element particular dels textos musicals; els fonemes. Podem entendre la música com la composició de diversos elements entre els quals podem trobar el text. Els models que construïm tenen en compte el context complementari del text. El context són tots aquells aspectes musicals que complementen el text, dels quals hem utilitzat en aquest tesi: la estructura de la composició musical, la estructura de les frases melòdiques i els accents rítmics. Des d’aquesta prespectiva analitzem no només les característiques acústiques de baix nivell, que representen el timbre musical dels fonemes, sinó també les característiques d’alt nivell en les quals es fa patent el context complementari. En aquest treball proposem models probabilístics específics que representen com les transicions entre fonemes consecutius de veu cantanda es veuen afectats per diversos aspectes del context complementari. El context complementari que tractem aquí es desenvolupa en el temps en funció de les característiques particulars de cada tradició musical. Per tal de modelar aquestes característiques hem creat corpus i conjunts de dades de dues tradicions musicals que presenten una gran riquesa en aquest aspectes; la música de l’opera de Beijing i la música makam turc-otomana. Les dades són de diversos tipus; enregistraments d’àudio, partitures musicals i metadades. Des d’aquesta prespectiva els models proposats poden aprofitar-se tant de les dades en si mateixes com del coneixement específic de la tradició musical per a millorar els resultats de referència actuals. Com a resultat de referència prenem un reconeixedor de fonemes basat en models ocults de Markov (Hidden Markov Models o HMM), una metodologia abastament emprada per a detectar fonemes tant en la veu cantada com en la parlada. Presentem millores en els processos comuns dels reconeixedors de fonemes actuals, ajustant-los a les característiques de les tradicions musicals estudiades. A més de millorar els resultats de referència també dissenyem models probabilistics basats en xarxes dinàmiques de Bayes (Dynamic Bayesian Networks o DBN) que respresenten la relació entre la transició dels fonemes i el context complementari. Hem creat dos models diferents per dos aspectes del context complementari; la estructura de la frase melòdica (alt nivell) i la estructura mètrica (nivell subtil). En un dels models explotem el fet que la duració de les síl·labes depén de la seva posició en la frase melòdica. Obtenim aquesta informació sobre les frases musical de la partitura i del coneixement específic de la tradició musical. En l’altre model analitzem com els atacs de les notes vocals, estimats directament dels enregistraments d’àudio, influencien les transicions entre vocals i consonants consecutives. A més també proposem com detectar les posicions temporals dels atacs de les notes en les frases melòdiques a base de localitzar simultàniament els accents en un cicle mètric musical. Per tal d’evaluar el potencial dels mètodes proposats utlitzem la tasca específica d’alineament de text amb àudio. Cada model proposat millora la precisió de l’alineament en comparació als resultats de referència, que es basen exclusivament en les característiques acústiques tímbriques dels fonemes. D’aquesta manera validem la nostra hipòtesi de que el coneixement del context complementari ajuda a la detecció automàtica de text musical, especialment en el cas de veu cantada amb acompanyament instrumental. Els resultats d’aquest treball no consisteixen només en metodologies teòriques i dades, sinó també en eines programàtiques específiques que han sigut integrades a Dunya, un paquet d’eines creat en el context del projecte de recerca CompMusic, l’objectiu del qual és promoure l’anàlisi computacional de les músiques del món. Gràcies a aquestes eines demostrem també que les metodologies desenvolupades es poden fer servir per a altres aplicacions en el context de la educació musical o la escolta musical enriquida.

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Книги з теми "VOICE SIGNALS"

Manfredi, Claudia, ed. Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy. Florence: Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-027-6.

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The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference.

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VoIP voice and fax signal processing. Hoboken, NJ: Wiley, 2008.

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Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-470-7.

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Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2009. http://dx.doi.org/10.36253/978-88-6453-096-3.

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Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6655-011-2.

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Juang, Jer-Nan. Signal prediction with input identification. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

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Martin, Ann. VOICE - a spectrogram computer display package. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1990.

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1954-, Lawlor Leonard, ed. Voice and phenomenon: Introduction to the problem of the sign in Husserl's phenomenology. Evanston, Ill: Northwestern University Press, 2011.

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Sacks, Oliver W. Seeing voices: A journey into the world of the deaf. Berkeley: University of California Press, 1989.

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Sacks, Oliver W. Seeing voices: A journey into the world of the deaf. New York: Vintage Books, 2000.

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Частини книг з теми "VOICE SIGNALS"

Bäckström, Tom. "Voice Activity Detection." In Signals and Communication Technology, 185–203. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50204-5_13.

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Herzel, Hanspeter, Joachim Holzfuss, Zbigniew J. Kowalik, Bernd Pompe, and Robert Reuter. "Detecting Bifurcations in Voice Signals." In Nonlinear Analysis of Physiological Data, 325–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-71949-3_19.

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Sondhi, M. Mohan. "Adaptive Echo Cancelation for Voice Signals." In Springer Handbook of Speech Processing, 903–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-49127-9_45.

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Kanas, V. G., I. Mporas, H. L. Benz, N. Huang, N. V. Thakor, K. Sgarbas, A. Bezerianos, and N. E. Crone. "Voice Activity Detection from Electrocorticographic Signals." In IFMBE Proceedings, 1643–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_405.

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Chaloupka, Josef, Jan Nouza, Jindrich Zdansky, Petr Cerva, Jan Silovsky, and Martin Kroul. "Voice Technology Applied for Building a Prototype Smart Room." In Multimodal Signals: Cognitive and Algorithmic Issues, 104–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00525-1_10.

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Icaza, Daniel, Juan-Carlos Cobos-Torres, Geovanny Genaro Reivan-Ortiz, and Federico Córdova Gonzalez. "Processing of Voice Signals in Telecommunications Systems Using MATLAB." In Artificial Intelligence, Computer and Software Engineering Advances, 177–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68080-0_13.

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Mourad, Talbi. "Arabic Speech Recognition by Stationary Bionic Wavelet Transform and MFCC Using a Multi-layer Perceptron for Voice Control." In Signals and Communication Technology, 69–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93405-7_4.

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Torres, D., and C. A. Ferrery. "Correcting HNR Estimation in Voice Signals Based on Periodicity Detection." In V Latin American Congress on Biomedical Engineering CLAIB 2011 May 16-21, 2011, Habana, Cuba, 1190–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-21198-0_302.

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Kang, Sangki, and Yongserk Kim. "A Dissonant Frequency Filtering for Enhanced Clarity of Husky Voice Signals." In Text, Speech and Dialogue, 517–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_65.

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Várallyay, György. "SSM – A Novel Method to Recognize the Fundamental Frequency in Voice Signals." In Lecture Notes in Computer Science, 88–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76725-1_10.

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Тези доповідей конференцій з теми "VOICE SIGNALS"

Herzel, Hanspeter, and Robert Reuter. "Biphonation in voice signals." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51002.

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Berdibaeva, Gulmira K., Oleg N. Bodin, Valery V. Kozlov, Dmitry I. Nefed'ev, Kasymbek A. Ozhikenov, and Yaroslav A. Pizhonkov. "Pre-processing voice signals for voice recognition systems." In 2017 18th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). IEEE, 2017. http://dx.doi.org/10.1109/edm.2017.7981748.

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Kolchenko, Liliia V., and Rustem B. Sinitsyn. "Nonparametric filter for voice signals." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011, edited by Ryszard S. Romaniuk. SPIE, 2011. http://dx.doi.org/10.1117/12.905174.

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"VOICE SIGNALS CHARACTERIZATION THROUGH ENTROPY MEASURES." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001065401630170.

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Rosinova, Marianna, Martin Lojka, Jan Stas, and Jozef Juhar. "Voice command recognition using EEG signals." In 2017 International Symposium ELMAR. IEEE, 2017. http://dx.doi.org/10.23919/elmar.2017.8124457.

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Setubal, Phabio J., Sidnei N. Filho, and Rui Seara. "Segmentation of singing voice within music signals." In Optics East, edited by John R. Smith, Tong Zhang, and Sethuraman Panchanathan. SPIE, 2004. http://dx.doi.org/10.1117/12.571280.

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Martin, Jose Francisco, Raquel Fernandez-Ramos, Jorge Romero-Sanchez, and Francisco Rios. "Signals voice biofeedback for speech fluency disorders." In Microtechnologies for the New Millennium 2003, edited by Angel Rodriguez-Vazquez, Derek Abbott, and Ricardo Carmona. SPIE, 2003. http://dx.doi.org/10.1117/12.499047.

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Salma, Chekili, Belhaj Asma, and Bouzid Aicha. "Organic voice pathology classification." In 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2017. http://dx.doi.org/10.1109/ssd.2017.8166981.

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Jaramillo, Juan Sebastian Hurtado, Diego Guarin, and Alvaro Angel Orozco. "Pseudo-periodic surrogate data method on voice signals." In 2012 11th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2012. http://dx.doi.org/10.1109/isspa.2012.6310604.

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Zouhir, Youssef, and Kais Ouni. "Parameterization of speech signals for robust voice recognition." In 2014 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM). IEEE, 2014. http://dx.doi.org/10.1109/cistem.2014.7076915.

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Звіти організацій з теми "VOICE SIGNALS"

Herrnstein, A. Start/End Delays of Voiced and Unvoiced Speech Signals. Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/15006006.

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ARMY SIGNAL CENTER AND FORT GORDON GA. Army Communicator. Voice of the Signal Regiment. Volume 33, Number 2, Spring 2008. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada494974.

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Hrynick, Tabitha, and Megan Schmidt-Sane. Note d’Orientation sur l’Engagement Communautaire Concernant la Riposte Contre la Flambée Epidémique de Choléra dans la Région Afrique de l’Est et Australe. Institute of Development Studies, May 2023. http://dx.doi.org/10.19088/sshap.2023.008.

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Les flambées épidémiques de choléra s’intensifient dans la région Afrique de l’Est et australe (ESAR) depuis janvier 2023, avec une transmission généralisée et étendue au Malawi et au Mozambique, ainsi que des flambées épidémiques signalées en Tanzanie, en Afrique du Sud, au Zimbabwe, au Burundi et en Zambie.1 Il existe un risque de propagation accrue causée par les effets du cyclone Freddy, qui a frappé Madagascar, le Malawi et le Mozambique en mars 2023. Les flambées épidémiques se poursuivent en Somalie, en Éthiopie, au Kenya et au Soudan du Sud, où les pays sont confrontés à la sécheresse suite à plusieurs saisons des pluies consécutives lors desquelles les précipitations n’ont pas été assez abondantes.1 Le contexte de riposte dans la région ESAR est complexe. Cela est dû aux ressources limitées en santé publique, y compris les pénuries de vaccins par voie orale contre le choléra, et aux nombreuses urgences sanitaires et humanitaires simultanées, y compris la réapparition du poliovirus sauvage. L’engagement communautaire dans les ripostes contre les flambées épidémiques de choléra est essentiel, en particulier lorsque l’impact de la COVID-19 continuent de se faire sentir dans la région, notamment en ce qui concerne la confiance dans la santé publique et les mesures liées à la vaccination.2,3 La présente note d’orientation a pour objectif d’aider les ministères de la Santé, l’UNICEF et d’autres partenaires de la riposte à concevoir et à mettre en œuvre un engagement communautaire efficace, axé sur la communauté et basé sur des données afin de répondre à la flambée épidémique de choléra. Cette note d’orientation a été rédigée en avril 2023 par Megan Schmidt-Sane et Tabitha Hrynick (IDS), avec la contribution de Stellar Murumba (Internews), Ngonidzashe Macdonald Nyambawaro (IFRC), Eva Niederberger (Anthrologica), Santiago Ripoll (IDS), Nadine Beckmann (LSHTM), Mariana Palavra (UNICEF), et Rachel James (UNICEF). Cette note d’orientation s’inspire de travaux antérieurs sur le choléra réalisés par la Plateforme Social Science in Humanitarian Action (SSHAP).

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