Frequency and Voice: Perspectives in the Time Domain

Roark, Rick M.

doi:10.1016/j.jvoice.2005.12.009

Cited by 43 publications

(36 citation statements)

References 25 publications

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“…It would be interesting to compare the F 0 estimation algorithms studied here, including the approaches for combining the individual F 0 estimation algorithms, for other sustained vowels (most relevant would be the other corner vowels, which are also sometimes used in voice quality assessment 4 ). Future work could also investigate more sophisticated combinations of F 0 estimation algorithms to build on the promising results of this study.…”

Section: Discussion and Summarymentioning

confidence: 99%

“…Since there is no universally single best F 0 estimation algorithm 3,4 and different F 0 estimation algorithms may be in their optimal setting under different signal conditions, it is possible that combining the outputs of the F 0 estimation algorithms could lead to improved F 0 estimates. Recently, Tsanas et al 10 proposed a simple ensemble approach to obtain the F 0 time series by introducing fixed weights for three of the F 0 estimation algorithms described in the preceding sections (PRAAT 1 , RAPT, and SHRP).…”

Section: B Information Fusion With Adaptive Kfmentioning

confidence: 99%

“…2 Typically, F 0 is evaluated over short-term time intervals and the time course of the F 0 values over the entire speech signal is known as F 0 time series (or F 0 contour). The existing difficulties in accurate F 0 estimation are well reported in the speech signal processing literature, with an excellent summary found in a study authored by Talkin. 3 According to Talkin these difficulties include: 3 (1) F 0 is time-varying, and may change between vocal cycles, (2) sub-harmonics (components of a waveform whose frequency is an integer fraction of the F 0 ) appear frequently, (3) F 0 may vary widely over successive vocal cycles, although often large F 0 variations are assumed to be artifacts of the estimation algorithm because such abrupt changes seem fairly rare, (4) vocal tract resonances affect the vocal folds (that is, there is feedback from the vocal tract to the vocal folds 2 ) resulting in harmonics which are multiples of the actual F 0 , (5) it is difficult to estimate F 0 at voice onset and offset (due to transient effects), (6) there is considerable inter-observer variability on the actual values of F 0 , and (7) periodic background noise might be challenging to differentiate from breathy voiced speech (their spectra may be similar). Additional problems include differentiating between voiced and unvoiced segments of speech, and specific cases which are very hard to deal with (e.g., where the signal is of extremely short duration).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

Tsanas

Zañartu

Little³

et al. 2014

The Journal of the Acoustical Society of America

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There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F 0 ) of speech signals. This study examines ten F 0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F 0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F 0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F 0 estimates, and the proposed KF approach resulted in a $16% improvement in accuracy over the best single F 0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F 0 estimation is required.

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Section: Discussion and Summarymentioning

confidence: 99%

Section: B Information Fusion With Adaptive Kfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

Tsanas

Zañartu

Little³

et al. 2014

The Journal of the Acoustical Society of America

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“…Since then, it has been used in various fields such as biomedical signal analysis [31], Hurst exponent estimation [32], speech processing [33], or texture analysis [34]. EMD aims at decomposing sequentially a given signal x n m ½ f g into a sum of Amplitude and Frequency Modulated (AM/FM) zero mean oscillatory signal, s nk m ½ f g, referred to as Intrinsic Mode Functions (IMFs), plus a non-zero meanlow-degree polynomial remainder.…”

Section: Emdmentioning

confidence: 99%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

150

122

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Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

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“…motor, vibrator, resonator and articulator. The motor part is responsible for the muscle movement to generate the vibration, vibration are generated by the glottis based on the 'muscle movement trigger' by the nerves system, resonators includes throat, mouth, the nasal and head cavities whereas articulation is performed by the tongue, lips, teeth, hard and soft palate [17]. The normal function of these system depends on the pathology of voice production system as well as the entire body as presence and concentration of different body fluid has a different impact on speaking voice.…”

Section: Introductionmentioning

confidence: 99%

Screening Upper Respiratory Diseases Using Acoustics Parameter Analysis of Speaking Voice

Bothe¹

2017

IJBSBE

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This paper presents the acoustic analysis of speaking voice for screening some of the upper respiratory diseases. The six acoustic parameters of speaking voice were analysed, which were recorded while reading the specific text. The text to read was designed considering phonetic categories of words used and their order of occurrence in the text. The appropriate number of words was combined to form the desired text to read for extracting maximum information from the upper respiratory system. Then recorded speaking voice was analysed for disease-specific corresponding variation in Total Harmonic Distortion (THD),

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Frequency and Voice: Perspectives in the Time Domain

Cited by 43 publications

References 25 publications

Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Screening Upper Respiratory Diseases Using Acoustics Parameter Analysis of Speaking Voice

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