A High Resolution Pitch Detection Algorithm Based on AMDF and ACF

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance.

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“…The fundamental frequency is the reciprocal of the pitch period. For the radar signal, x i ( l ), the ACF is generally defined [33] as:

R_{i} false(k false) = true \sum_{l = 1}^{N - k} x_{i} false(l false) x_{i} false(l + k false)

…”

Section: Methodsmentioning

confidence: 99%

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

Chen

Sheng

Zhang

et al. 2017

Sensors

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“…A segment is taken as voiced if the average magnitude of the selected segment is greater than 33% of the Av g . This algorithm for pitch detection is much simpler than the one proposed by Abdullah et al [12] and the one proposed by Suma et al [17]. In the proposed method of pitch detection, the given speech signal is converted to a 3-level signal before computing autocorrelation (ACF) and average magnitude difference function (AMDF).…”

Section: The Proposed Pitch Detection Scheme Based Onmentioning

confidence: 99%

“…978-1-4799-3914-5114/$3l.00 ©2014 IEEE researchers [7,8] and AMDF [9,10,11] to improve the reliability and performance of pitch detection schemes. A new pitch detection scheme utilizing the advantageous features of both ACF and AMDF is proposed by Abdullah et al [12]. In their work, the speech signal is fIrst passed through four band pass filter and ACF and AMDF are calculated for each filter output.…”

Section: Introductionmentioning

confidence: 99%

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A new pitch detection scheme based on ACF and AMDF

Kumar

Bhattacharya

Patel

2014

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

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A new pitch detection scheme has been proposed based on the short-time autocorrelation function (ACF) and average magnitude difference function (AMDF). The performance of the proposed scheme has been evaluated, through simulation, in a complete speech analysis-synthesis system. For detection of pitch, local maxima of ACF and local mlOlma of AMDF values are computed. To reduce computational complexity, the original speech signal is converted into a three level signal before computing ACF and AMDF. Synthesized speech quality, computational complexity and time taken during simulation are the parameters that have been considered while comparing this system with the analysis-synthesis systems that use autocorrelation, cepstru m and wavelet based pitch detection methods. Results for different speech signals show that this new method of pitch detection is the best in terms of speech quality and computational complexity.

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“…Efficient PDA and SAD methods can considerably improve the performance of large speech processing systems, such as speech recognition, speaker identification and speech coding systems. For this reason, advanced algorithms have been proposed for robust PDA and SAD in adverse acoustic environments [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Normalization Method for Autocorrelation Function for Pitch Detection and for Speech Activity Detection

Lin¹,

Shao

2018

Interspeech 2018

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Autocorrelation functions (ACF) have been used in various pitch detection algorithms (PDA) and voicing-feature based speech activity detection (SAD) techniques. Speech is assumed to be stationary over a short-term window, and a Hanning window is typically applied in the calculation of ACF. As a result of windowing, the ACF tapers as the autocorrelation lags increase. Boersma demonstrated that the tapering effect could be compensated for by dividing the ACF of the windowed signal by the autocorrelation of the windowing function itself, referred to as wACF hereafter. We recently found that wACF could cause overcompensation and therefore, result in errors in pitch detection. In this paper, a novel normalization method, eACF, is proposed that can both mitigate the tapering effect and minimize the overcompensation. The new method is evaluated on synthetic speech and on the TIMIT database with various types of additive noise at different signal-to-noise (SNR) ratios. The results show that the new method leads to better performance both in terms of pitch detection and speech activity detection. In this paper, we also investigate the scenarios where applying the wACF method is advantageous and where it is not.

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A High Resolution Pitch Detection Algorithm Based on AMDF and ACF

Cited by 14 publications

References 9 publications

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

A new pitch detection scheme based on ACF and AMDF

A Novel Normalization Method for Autocorrelation Function for Pitch Detection and for Speech Activity Detection

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