L.R. Rabiner. On the Use of Autocorrelation Analysis for Pitch Detection. IEEE Transactions on Acoustics, Speech, and Signal Processing, 25(1):24--33, February 1977.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....two stereo channels contained the strongest saxophone signal, so it was extracted to a mono sound file and down sampled to 22,050Hz for the next step. 3.2 Pitch Extraction and Segmentation Pitch estimation was performed using an autocorrelation technique on overlapping windows. Autocorrelation [15, 16] computes the correlation between the signal and a copy of the signal shifted by different amounts of time. When the shift is equal to a multiple of fundamental periods, the correlation will be high. In theory, one simply finds the first peak in the autocorrelation. In practice, there are small ....

Rabiner, L. On the use of autocorrelation analysis for pitch detection. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-25 (1). 24-33.

....within each speech frame. We apply this correction method to an AMDF based PDA. The experimental results show that probabilistic error correction is indeed successful in reducing the errors that occur during pitch detection. 2. AMDF BASED PITCH DETECTION ALGORITHM Many PDAs have been developed [1, 2, 3, 5, 6, 7, 8, 12, 13, 14]. The autocorrelation function and the average magnitude difference function (AMDF) 8, 12] are the two most frequently used time domain PDAs. The AMDF pitch detection algorithm is chosen in our study is because it has relatively low computational cost and is easy to implement. Basic Algorithm: ....

....error correction is indeed successful in reducing the errors that occur during pitch detection. 2. AMDF BASED PITCH DETECTION ALGORITHM Many PDAs have been developed [1, 2, 3, 5, 6, 7, 8, 12, 13, 14] The autocorrelation function and the average magnitude difference function (AMDF) [8, 12] are the two most frequently used time domain PDAs. The AMDF pitch detection algorithm is chosen in our study is because it has relatively low computational cost and is easy to implement. Basic Algorithm: For each frame k, the short term difference function AMDF is defined as follows: AMDFn(j) ....

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L.R. Rabiner. On the use of autocorrelation analysis for pitch detection. IEEE Trans. ASSP, ASSP-25:24--33, 1977.

.... for controlling synthesizers from this pitch information and absolutely necessary for pitch synchronous algorithms such as PSOLA techniques [1] Various methods have been proposed for the determination of the pitch as a function of time (pitch tracking) They use either the autocorrelation factor [2], other physical [3, 4] or geometric [5] criteria, least square fitting [6] pattern recognition [7] or even neural networks [8] Arfib and Delprat use in [9] the inverse FFT of the sound spectrum modulus limited to the positive frequency. In this article, we propose a new composition of two ....

Lawrence R. Rabiner, "On the Use of Autocorrelation Analysis for Pitch Detection," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 25, no. 1, pp. 24--33, February 1977.

.... function makes the estimate of the lag of the peak too low, and therefore the pitch estimate too high (e.g. for 3 periods of a sine wave in a Hanning window, the difference is 6 ) One method commonly used to overcome the first problem, is to filter away all frequencies above 900 Hz (Rabiner, 1977), which should kill all formants except the first, and estimate the pitch from the second maximum. This is not a very robust method, because we often run into higher formants below 900 Hz and fundamental frequencies above 900 Hz. Other methods to lose the formant include centre clipping, spectral ....

.... This estimation can easily be seen to be exact for the constant signal x(t) 1 (without subtracting the mean, of course) for periodic signals, it brings the autocorrelation peaks very near to 1 (see figure 1) The need for this correction seems to have gone by unnoticed in the literature; e.g. Rabiner (1977) states that no matter which window is selected, the effect of the window is to taper the autocorrelation function smoothly to 0 as the autocorrelation index increases . With equation (9) this is no longer true. The accuracy of the algorithm is determined by the reliability of the estimation ....

Rabiner, L.R. (1977): "On the use of autocorrelation analysis for pitch detection", IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-25: 24-33.

....sampling rate is 32000 Hz. All three methods use the same analysis frequencies to determine the frequency and amplitude at each time step. These analysis frequencies are the harmonic components with the fundamental frequency determined algorithmically using an autocorrelation based pitch tracker [8]. The analysis frequencies are generally worse than the estimated frequencies of all three methods. The resulting amplitude and frequency tracks can be seen in figure 2 for the fundamental of the 300 Hz test signal. The peak interpolation results are shown top left, the analytic derivation ....

L. R. Rabiner, On the use of autocorrelation analysis for pitch detection, IEEE Trans. Acoustics, Speech and signal processing. Vol. ASSP-25, No 1, February 1977.

....or signal conditioning, stage is included to minimize signal properties that are not germane to pitch tracking, such as the formant structure. Typically this can include some band pass filtering, though it can include whitening the signal with an auto regressive model and nonlinear techniques [8]. Generally pitch candidates are generated through a two dimensional function f(t, p) of the time t and pitch period p, that assigns higher values to more likely pitch candidates at each time. It can be formulated in several domains, including time [6] 9] autocorrelation [8] 10] cepstrum [4] ....

....nonlinear techniques [8] Generally pitch candidates are generated through a two dimensional function f(t, p) of the time t and pitch period p, that assigns higher values to more likely pitch candidates at each time. It can be formulated in several domains, including time [6] 9] autocorrelation [8][10] cepstrum [4] and ACOLS [2] All of these techniques extract short segments of speech with explicit or implicit windows. The post processing stage consists of taking the likely pitch candidates and choosing one for each input frame. The most common approach is to use dynamic programming to ....

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Rabiner L. R. "On the Use of Autocorrelation Analysis for Pitch Detection". IEEE Transactions on ASSP, vol. 25, pp. 24-33, 1977.

....is clearly effective, the implementation discussed required a parallel processor for the heavy load, and was certainly not appropriate for the project discussed in this article. Autocorrelation algorithms have been successfully used in speech processing for some time. The autocorrelation function (Rabiner 1977) OE x (m) 1 N N Gamma1 X n=0 [x(n l)w(n) x(n l m)w(n m) 0 m M 0 Gamma 1 will produce maxima at the period of the input signal. N is the sample window length, n is the index of the current sample, m is the index of the delayed sample, w( is window function, and l is the ....

Rabiner, L. R. 1977. "On the Use of Autocorrelation Analysis for Pitch Detection." IEEE Transactions on Acoustics, Speech, and Signal Processing 25(1): 24-33.

....the multitude of pitch determination algorithms proposed, an algorithm that is absolutely reliable remains to be found, especially in highly degraded signal conditions. Among the many different methods, one of the oldest and most celebrated is based on short time autocorrelation (STA) analysis [1]. The method proposed in this paper is again based on STA analysis, but this is now applied to the outputs of an auditory model known as the Lyon s cochlear model [2] This new algorithm is strongly inspired by the Licklider s duplex theory of pitch perception [3] subsequently adopted by M. Slaney ....

L.R. Rabiner, "On the use of autocorrelation analysis for pitch detection", Proc. IEEE, Vol. 58, 1970, pp.707-712.

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L.R. Rabiner. On the Use of Autocorrelation Analysis for Pitch Detection. IEEE Transactions on Acoustics, Speech, and Signal Processing, 25(1):24--33, February 1977.

No context found.

Rabiner, L. R., On the use of autocorrelation analysis for pitch detection. IEEE Trans. ASSP, Vol. ASSP-25, No. 1, February 1977.

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Rabiner, L. "On the use of autocorrelation analysis for pitch detection", IEEE Trans. ASSP, 25 (1): 24-33, 1977.

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L. R. Rabiner. On the use of autocorrelation analysis for pitch detection. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-25:24-- 33, 1977.

No context found.

L. R. Rabiner. On the use of autocorrelation analysis for pitch detection. IEEE Transactions on Acoustics, Speech and Signal Processing, 25(1):24--33, 1977.

No context found.

L.R. Rabiner. On the use of autocorrelation analysis for pitch detection. IEEE Trans. ASSP, ASSP-25:24#33, 1977.

No context found.

L. R. Rabiner, "On the Use of Autocorrelation Analysis for Pitch Detection," IEEE Trans. ASSP,Vol. 25, No.1, pp. 24#33, Feb. 1977.

No context found.

L. Rabiner, "On use of autocorrelation analysis for pitch detection," IEEE Trans. of ASSP., vol. 25, pp. 23-- 33, Feb. 1977.

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