Performance measurement of a novel pitch detection scheme based on weighted autocorrelation for speech signals

Kumar, Sandeep

doi:10.1007/s10772-019-09634-5

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…e fierce debate on missing fundamental led to the calculation of fundamental frequency, and many algorithms flourished to extract and track the pitch of a signal. Among the most notables are AMDF [18][19][20], autocorrelation [13][14][15][16][21][22][23][24][25], cepstrum [26][27][28], harmonic product spectrum, period histograms [29][30][31], parallel processing methods [32][33][34], simplified inverse filter tracking (SIFT) [35], comb filters [36], data reduction [37], LPC-based spectral equalization (unpublished), spectral sieves [38], harmonic spacings and structures [39][40][41], LPC inverse filtering [20], feature based [42], IPTA [43], harmonic pattern recognition [44], envelop analysis, threshold-crossing analysis (ZXABE, TABE, TTABE) [45,46], subharmonic summation [47], subband processing [48], superresolution [49], two-way mismatch [50], resolution improvement [51], TEMPO [52], RAPT (NCCF) [53], instantaneous frequency [54][55]…”

Section: Algorithmsmentioning

confidence: 99%

Harmonic Differences Method for Robust Fundamental Frequency Detection in Wideband and Narrowband Speech Signals

Parlak

Altun

2021

Mathematical Problems in Engineering

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In this article, a novel pitch determination algorithm based on harmonic differences method (HDM) is proposed. Most of the algorithms today rely on autocorrelation, cepstrum, and lastly convolutional neural networks, and they have some limitations (small datasets, wideband or narrowband, musical sounds, temporal smoothing, etc.), accuracy, and speed problems. There are very rare works exploiting the spacing between the harmonics. HDM is designed for both wideband and exclusively narrowband (telephone) speech and tries to find the most repeating difference between the harmonics of speech signal. We use three vowel databases in our experiments, namely, Hillenbrand Vowel Database, Texas Vowel Database, and Vowels from the TIMIT corpus. We compare HDM with autocorrelation, cepstrum, YIN, YAAPT, CREPE, and FCN algorithms. Results show that harmonic differences are reliable and fast choice for robust pitch detection. Also, it is superior to others in most cases.

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Section: Algorithmsmentioning

confidence: 99%

Harmonic Differences Method for Robust Fundamental Frequency Detection in Wideband and Narrowband Speech Signals

Parlak

Altun

2021

Mathematical Problems in Engineering

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“…For example, it can be used to extract the pitch period of speech signals by finding the highest peak in the AC. In [3], a weighted AC function is used. The weighting is done using the average magnitude difference function (AMDF) which has a notch where the lag is equal to the period.…”

Section: Introductionmentioning

confidence: 99%

Robust Period Estimation of Automated Cutting Systems by Improved Autocorrelation & Linear Regression Techniques

McAtear

Gielen²,

Madhu

2021

2020 28th European Signal Processing Conference (EUSIPCO)

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Condition monitoring is an important asset in the industry to improve the safety and efficiency of the production chain. However, in heavy machinery -such as edge trimmers in steel mills -it is often impractical and unsafe to install intrusive sensors to get the data needed for condition monitoring. Nonintrusive monitoring techniques based, e.g., on acoustic data captured by microphones placed in the vicinity of the assembly being monitored are attractive options. Our application deals with the acoustic monitoring of rotational blades cutting steel strips at high speeds. Knowing the correct period of the cutting process is important for quality evaluation purposes. We propose two novel robust methods to estimate the periodicity based on the audio captured by a microphone near the blades. One is an improved autocorrelation function and the other is based on linear regression, both using incorporating an novel test for the correctness of the estimated period. We compare our methods against the standard autocorrelation-based periodicity measurement techniques on real data recordings. The proposed method estimates the correct period about 87% of the time, compared to an accuracy of only 51% using standard periodicity measurement approaches.

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Speech quality evaluation for different pitch detection algorithms in LPC speech analysis–synthesis system

Kumar

Singh

Agarwal

et al. 2020

Int J Speech Technol

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Performance measurement of a novel pitch detection scheme based on weighted autocorrelation for speech signals

Cited by 7 publications

References 14 publications

Harmonic Differences Method for Robust Fundamental Frequency Detection in Wideband and Narrowband Speech Signals

Harmonic Differences Method for Robust Fundamental Frequency Detection in Wideband and Narrowband Speech Signals

Robust Period Estimation of Automated Cutting Systems by Improved Autocorrelation & Linear Regression Techniques

Speech quality evaluation for different pitch detection algorithms in LPC speech analysis–synthesis system

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