Bibliography: Monophonic Fundamental Frequency Estimation

• Abe, T., Kobayashi, T., and Imai, S. [1995]. Harmonics tracking and pitch extraction based on instantaneous frequency. In Proceedings of the 1995 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1. pp. 756 – 9.


• Paper using instantaneous frequency measurement to adapt the center frequency of a series of bandpass filters. Decisions are made on the updated values of the different filters to prevent estimating the same partial twice. This is a method that could be classified under the spectral location category but which does not use a fixed harmonic pattern to compare the observation spectrum with. By the way it is not very clear how the filters are chosen (what frequencies are chosen at the start of the algorithm).


• Abu-Shikhah, N. and Deriche, M. [1999]. A novel pitch estimation technique using the {Teager} energy function. In Proceedings of the IEEE Fifth International Symposium on Signal Processing and Its Applications.


• Interesting method that uses the instantaneous energy of the signal, the Teager Energy Function (TEF). This value takes into account the dynamic nature of speech (or music): the past samples affect present samples and future values. The estimation of the fudamental is done by sorting the peaks of the TEF and measuring the time lag between peaks of significant amplitude. Another example of fundamental frequency that does not quite fit Klapuri's typology.


• Brown, J. C. [1992]. Musical fundamental frequency tracking using a pattern recognition method. Journal of the Acoustical Society of America, vol. 92(3): pp. 1394 – 1402.


• Typical example of harmonic pattern matching method exploiting the fact on a logarithmic scale, no matter the fundamental frequency, the interval between harmonic partials is the same.


• de Cheveigne, A. and Kawahara, H. [2001]. Comparative evaluation of f0 estimation algorithms. In Proceedings of Eurospeech.


• Interesting attempt to follow on Rabiner's example by establishing a methodology for comparison and evaluation of different fundamental estimators.


• Doval, B. and Rodet, X. [1991]. Estimation of fundamental frequency of musical sound signals. In Proceedings of the 1991 IEEE International Conference on Acoustics, Speech and Signal Processing.


• Article presenting a Bayesian approach to fundamental frequency estimation where the fundamental is identified as the frequency candidate maximizing the likelihood of the observations, assuming that the harmonics of the sound are located in Gaussian regions around the true harmonics.


• Doval, B. and Rodet, X. [1993]. Fundamental frequency estimation and tracking using maximum likelihood harmonic matching and HMM's. In Proceedings of the 1993 IEEE International Conference on Acoustics, Speech and Signal Processing.


• Logical extension of the previous article by Doval and Rodet where partial tracking is implemented using HMMs.


• Hess, W. [1983]. Pitch Determination of Speech Signals. Springer-Verlag.


• Key reference for a very complete list of fundamental frequency estimation techniques applied to speech signals. The McGill catalogue Id is : TK7882 S65 H47 1983. It has been updated by another book of the same author, in 1991 : `Advances in Speech Signal Processing'


• Klapuri, A. [2004]. Signal Processing Methods for the Automatic Transcription of Music. Ph.D. thesis, Tampere University of Technology.


• Very clear work. I mainly based my work on Chapter 3 where a global overview of fundamental frequency estimation techniques is given. I liked the attempt to classify the plethora of available methods.


• Lahat, M., Niederjohn, R. J., and Krubsack, D. A. [1987]. A spectral autocorrelation method for measurement of the fundamental frequency of noise-corrupted speech. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 35(6): pp. 741 – 50.


• An example of spectral interval fundamental extraction method. It also presents how this system can be used as a voiced/unvoiced classifier.


• Maher, R. C. and Beauchamp, J. W. [1994]. Fundamental frequency estimation of musical signals using a two-way mismatch procedure. Journal of the Acoustical Society of America, vol. 95(1): pp. 2254 – 63.


• Very valuable presentation of a fundamental estimation method that has been used in many applications including the SMS by Xavier Serra to perform pitch synchronous analysis.


• Meddis and Hewitt, M. [1991]. Virtual pitch and phase sensitivity of a computer model of the auditory periphery. I: Pitch identification. Journal of the Acoustical Society of America, vol. 89(6): pp. 2866 – 82.


• Important work that unifies the previously separated perceptual models of hearing. Although the periodicity inference is still under research, the global flow of the model is still a reference today. Especially the way the signal is preprocessed to simulate the transition from sound to auditory nerve signal.


• Noll, A. M. [1967]. Cepstrum pitch detection. Journal of the Acoustical Society of America, vol. 41(2): pp. 293 – 309.


• Presents the idea behind the cepstrum and explains the link between the autocorrelation function and the cepstrum. It is also very interesting to see this very often cited article.


• Rabiner, L. [1977]. On the use of autocorrelation anaysis for pitch detection. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 25(1): pp. 24 – 33.


• A very extensive study of the autocorrelation analysis for pitch detection in speech signal. The focus is on the different pre-processing steps that can be used in order to make the method robust to formants in speech.


• Rabiner, L., Cheng, M., Rosenberg, A., and McGonegal, C. [1976]. A comparative performance study of several pitch detection algorithms. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 24(5): pp. 399 – 418.


• One 0f the first attempt to actually compare different fundamental estimation techniques. The specific interest of this work relies in the attempt to define a methodology used for the evaluation.