Event-Based Instantaneous Fundamental Frequency Estimation From Speech Signals

Yegnanarayana, B.; Murty, K. Sri Rama

doi:10.1109/tasl.2008.2012194

Cited by 190 publications

(90 citation statements)

References 21 publications

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“…The second column in the table shows the number of voiced frames (V(n)) detected in the respective utterance. From the third column on, the numbers represent the GEs obtained from BC and AC speech (GE BC (n)/GE AC (n)), respectively, using Praat's AC and CC [14], YIN [3], SWIPE [4], and EBIFF [5] algorithms. As indicated by the GEs, BC speech produces much less errors compared to AC speech for almost all the methods.…”

Section: Pitch Reference Produced From Bone Conducted Speechmentioning

confidence: 99%

“…Again, results obtained using SWIPE algorithm are seemed to be little biased because the reference file is constructed based on the primary estimates obtained using this method. Though the instantaneous frequency based method [5] works by zero-frequency filtering, it is observed that the filtered signals produced by applying the algorithm on BC speech is more suitable for pitch estimation than AC speech. To summarize, when signals from both the AC and BC channels are available, pitch determination from BC speech can yield superior performance.…”

Section: Pitch Reference Produced From Bone Conducted Speechmentioning

confidence: 99%

“…Numerous pitch determination algorithms (PDA) [1]- [5] have also been developed, none of which, however, is perfect. Further, a common shortcoming of all types of the extracted features is the sensitivity to background noise.…”

Section: Introductionmentioning

confidence: 99%

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Pitch Determination from Bone Conducted Speech

Rahman

Shimamura

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper explores the potential of pitch determination from bone conducted (BC) speech. Pitch determination from normal air conducted (AC) speech signal can not attain the expected level of accuracy for every voice and background conditions. In contrast, since BC speech is caused by the vibrations that have traveled through the vocal tract wall, it is robust against ambient conditions. Though an appropriate model of BC speech is not known, it has regular harmonic structure in the lower spectral region. Due to this lowpass nature, pitch determination from BC speech is not usually affected by the dominant first formant. Experiments conducted on simultaneously recorded AC and BC speech show that BC speech is more reliable for pitch estimation than AC speech. With little human work, pitch contour estimated from BC speech can also be used as pitch reference that can serve as an alternate to the pitch contour extracted from laryngograph output which is sometimes inconsistent with simultaneously recorded AC speech. key words: bone conducted speech, air conducted speech, pitch frequency, laryngograph output, pitch reference

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Section: Pitch Reference Produced From Bone Conducted Speechmentioning

confidence: 99%

Section: Pitch Reference Produced From Bone Conducted Speechmentioning

confidence: 99%

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Pitch Determination from Bone Conducted Speech

Rahman

Shimamura

2016

IEICE Trans. Inf. & Syst.

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“…Algorithms for speech-based GCI detection have been widely evaluated using EGG-based references [12], [22], [24]. It is known that the synchronization of EGG and speech signals is affected by the propagation time from the talker's lips to the recording microphone that may be estimated and subtracted to synchronize the two signals.…”

Section: Evaluation Referencementioning

confidence: 99%

“…The multiscale product is a key element in the technique proposed in this paper. Detection of periodicity in the speech has also been explored through analysis of the autocovariance matrix of the speech signal [23], zero-frequency resonator [24] and empirical mode decomposition (EMD) [25]. These non model-based approaches are advantageous because they are well-rooted in signal processing and are not constrained by any particular speech model.…”

mentioning

confidence: 99%

Estimation of Glottal Closing and Opening Instants in Voiced Speech Using the YAGA Algorithm

Thomas

Guðnason

Naylor

2012

IEEE Trans. Audio Speech Lang. Process.

109

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Abstract-Accurate estimation of glottal closing instants (GCIs) and opening instants (GOIs) is important for speech processing applications that benefit from glottal-synchronous processing including pitch tracking, prosodic speech modification, speech dereverberation, synthesis and study of pathological voice. We propose the Yet Another GCI/GOI Algorithm (YAGA) to detect GCIs from speech signals by employing multiscale analysis, the group delay function, and -best dynamic programming. A novel GOI detector based upon the consistency of the candidates' closed quotients relative to the estimated GCIs is also presented. Particular attention is paid to the precise definition of the glottal closed phase, which we define as the analysis interval that produces minimum deviation from an all-pole model of the speech signal with closedphase linear prediction (LP). A reference algorithm analyzing both electroglottograph (EGG) and speech signals is described for evaluation of the proposed speech-based algorithm. In addition to the development of a GCI/GOI detector, an important outcome of this work is in demonstrating that GOIs derived from the EGG signal are not necessarily well-suited to closed-phase LP analysis. Evaluation of YAGA against the APLAWD and SAM databases show that GCI identification rates of up to 99.3% can be achieved with an accuracy of 0.3 ms and GOI detection can be achieved equally reliably with an accuracy of 0.5 ms.Index Terms-Dynamic programming, electroglottograph (EGG), glottal closing instants (GCIs), glottal opening instants (GOIs), group delay function, multiscale analysis, speech processing.

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Estimation of static and dynamic functional connectivity in resting‐state fMRI using zero‐frequency resonator

Das,

Sao,

Biswal

2024

Human Brain Mapping

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Resting‐state functional magnetic resonance imaging (rs‐fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level‐dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs‐fMRI is spontaneous event‐related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low‐valued inflection points. Here, we extract the event‐related temporal onsets from the rs‐fMRI time courses using a zero‐frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal‐to‐noise ratio (SNR) segments are also obtained in the rs‐fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR‐based CR distinguishes the coactivation and non‐coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR‐driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

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Event-Based Instantaneous Fundamental Frequency Estimation From Speech Signals

Abstract: Abstract-Exploiting

Cited by 190 publications

References 21 publications

Pitch Determination from Bone Conducted Speech

Pitch Determination from Bone Conducted Speech

Estimation of Glottal Closing and Opening Instants in Voiced Speech Using the YAGA Algorithm

Estimation of static and dynamic functional connectivity in resting‐state fMRI using zero‐frequency resonator

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