Open AccessProceedings Article
A Pitch Estimation Filter robust to high levels of noise (PEFAC)
Sira Gonzalez,Mike Brookes +1 more
- pp 451-455
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TLDR
PEFAC is presented, a fundamental frequency estimation algorithm that is able to identify the pitch of voiced frames reliably even at negative signal to noise ratios, and performs exceptionally well in both high and low levels of additive noise.Abstract:
We present PEFAC, a fundamental frequency estimation algorithm that is able to identify the pitch of voiced frames reliably even at negative signal to noise ratios. The algorithm combines non-linear amplitude compression, to attenuate narrow-band noise components, with a comb-filter applied in the log-frequency power spectral domain, whose impulse response is chosen to attenuate smoothly varying noise components. We compare the performance of our algorithm with that of other widely used algorithms on a subset of the TIMIT database and demonstrate that it performs exceptionally well in both high and low levels of additive noise.read more
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Journal ArticleDOI
PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise
Sira Gonzalez,Mike Brookes +1 more
TL;DR: PEFAC is presented, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios and performs well in both high and low levels of additive noise.
Journal ArticleDOI
A feature study for classification-based speech separation at low signal-to-noise ratios
TL;DR: This study systematically evaluates a range of promising features for classification-based separation using six nonstationary noises at the low SNR level of -5 dB, and proposes a new feature called multi-resolution cochleagram (MRCG), which experimental results show gives the best classification results among all evaluated features.
Journal ArticleDOI
rVAD: An unsupervised segment-based robust voice activity detection method
TL;DR: A modified version of rVAD is presented where computationally intensive pitch extraction is replaced by computationally efficient spectral flatness calculation, which significantly reduces the computational complexity at the cost of moderately inferior VAD performance, which is an advantage when processing a large amount of data and running on low resource devices.
Journal ArticleDOI
MMSE-Optimal Spectral Amplitude Estimation Given the STFT-Phase
Timo Gerkmann,Martin Krawczyk +1 more
TL;DR: A minimum mean squared error (MMSE) optimal estimator for clean speech spectral amplitudes is derived, which is applied in single channel speech enhancement and it is shown that the phase contains additional information that can be exploited to distinguish outliers in the noise from the target signal.
Journal ArticleDOI
A data-driven non-intrusive measure of speech quality and intelligibility
TL;DR: The NISA measure, NISA, is a new measure for estimating the quality and intelligibility of speech degraded by additive noise and distortions associated with telecommunications networks, based on a data driven framework of feature extraction and tree based regression.
References
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Journal ArticleDOI
YIN, a fundamental frequency estimator for speech and music
TL;DR: An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds, based on the well-known autocorrelation method with a number of modifications that combine to prevent errors.
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An international comparison of long‐term average speech spectra
Denis Byrne,Harvey Dillon,Khanh Tran,Stig Arlinger,Keith Wilbraham,Robyn M. Cox,Bjorn Hagerman,Raymond Hétu,Joseph Kei,C. Lui,Jürgen Kiessling,M. Nasser Kotby,Nasser H. A. Nasser,Wafaa A. H. El Kholy,Yasuko Nakanishi,Herbert J. Oyer,Richard Powell,Dafydd Stephens,Rhys Meredith,Tony Sirimanna,G. A. Tavartkiladze,Gregory I. Frolenkov,Soren Westerman,Carl Ludvigsen +23 more
TL;DR: The long-term average speech spectrum (LTASS) and some dynamic characteristics of speech were determined for 12 languages: English (several dialects), Swedish, Danish, German, French (Canadian), Japanese, Cantonese, Mandarin, Russian, Welsh, Singhalese, and Vietnamese.
Journal ArticleDOI
Measurement of pitch by subharmonic summation
TL;DR: It is argued that the favorable performance of the subharmonic-summation algorithm stems from its corresponding more closely with current pitch-perception theories than does the harmonic sieve.