Signal processing is a discipline dedicated to the engineering end of stochastic process inference and prediction, especially linear time series.
There are various translation difficulties for statisticians; “Testing”=“Detection”, “Linear Filter”=“ARIMA model”, estimation of parameters is system identification, estimation of hidden states is filtering and so on.
This is a very general note to mention that the field exists. Most useful information is under sub-fields, e.g. machine listening, for some signal processing tricks for audio, or feedback systems, for some models particularly appropriate to systems that accept their own output as input etc.
See also orthogonal decompositions. There are close connections to optimal control.
Anyway, I don’t need to explain that here; there are so many software engineers involved with it. the internet is full of interactive diagrammy textbooks to fill that niche.
But here are some notes on some nuggets of interest that I wasn’t sure where else to file.
Nothing to say here yet but I feel I should raid the literature of the EPFL Signal processing lab 2 who make a specialty of it.
See also the slightly more specialised and overlapping list of filter design resources
- Tom O’Haver has a free online textbook with extensive OCTAVE/MATLAB code, A Pragmatic Introduction to Signal Processing. Very skewed towards pure Fourier domain techniques.
- Textbook: Paolo Prandoni and Martin Vetterli, Signal Processing for Communications is available online. Vetterli is very smart at unexpected and enlightening perspectives; I’m a fan.
- Textbook: Antoniou has been generally recommended if you want to get hands-on ASAP. (Anto05)
- Textbook: Orfandis’ opus is free online. (Orfa96)
- Course notes/textbook: Oppenheim and Verghese, Signals, Systems, and Inference is free online.
- Numerical tours of signal processing gives python, julia and matlab tours of signal processing. Better consumed through their github repo.
- Antoniou, A. (2005) Digital signal processing: signals, systems and filters. . New York: McGraw-Hill
- Bartlett, M. S.(1946) On the Theoretical Specification and Sampling Properties of Autocorrelated Time-Series. Supplement to the Journal of the Royal Statistical Society, 8(1), 27–41. DOI.
- Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M.(2016) Time series analysis: forecasting and control. (Fifth edition.). Hoboken, New Jersey: John Wiley & Sons, Inc
- Chamberlin, H. (1985) Musical applications of microprocessors. (2nd ed.). Hasbrouck Heights, N.J: Hayden Book Co.
- Gray, R. M., & Davisson, L. D.(2010) An introduction to statistical signal processing. . Cambridge: Cambridge University Press
- Holan, S. H., Lund, R., & Davis, G. (2010) The ARMA alphabet soup: A tour of ARMA model variants. Statistics Surveys, 4, 232–274. DOI.
- Kailath, T., Sayed, A. H., & Hassibi, B. (2000) Linear estimation. . Upper Saddle River, N.J: Prentice Hall
- Kay, S. M.(1993) Fundamentals of statistical signal processing. . Englewood Cliffs, N.J: Prentice-Hall PTR
- Laroche, J. (2007) On the Stability of Time-Varying Recursive Filters. Journal of the Audio Engineering Society, 55(6), 460–471.
- Marple, S. L., Jr. (1987) Digital spectral analysis with applications.
- Moon, T. K., & Stirling, W. C.(2000) Mathematical methods and algorithms for signal processing. . Upper Saddle River, NJ: Prentice Hall
- Moorer, J. . (1974) The optimum comb method of pitch period analysis of continuous digitized speech. IEEE Transactions on Acoustics, Speech and Signal Processing, 22(5), 330–338. DOI.
- Narasimha, M. J., Ignjatovic, A., & Vaidyanathan, P. P.(2002) Chromatic derivative filter banks. IEEE Signal Processing Letters, 9(7), 215–216. DOI.
- Nyquist, H. (1928) Certain Topics in Telegraph Transmission Theory. Transactions of the American Institute of Electrical Engineers, 47(2), 617–644. DOI.
- Oppenheim, A. V., Schafer, R. W., & Buck, J. R.(1999) Discrete-time signal processing. (2nd ed.). Upper Saddle River, N.J: Prentice Hall
- Orfanidis, S. J.(1996) Introduction to signal processing. . Englewood Cliffs, N.J: Prentice Hall
- Pawar, S., & Ramchandran, K. (2015) A robust sub-linear time R-FFAST algorithm for computing a sparse DFT. ArXiv:1501.00320 [Cs, Math].
- Prandoni, P., & Vetterli, M. (2008) Signal processing for communications. . Lausanne: EPFL Press
- Proietti, T., & Luati, A. (2013) The Exponential Model for the Spectrum of a Time Series: Extensions and Applications (SSRN Scholarly Paper No. ID 2254038). . Rochester, NY: Social Science Research Network
- Qian, S., & Chen, D. (1994) Signal representation using adaptive normalized Gaussian functions. Signal Processing, 36(1), 1–11. DOI.
- Ragazzini, J. R., & Zadeh, L. A.(1952) The analysis of sampled-data systems. Transactions of the American Institute of Electrical Engineers, Part II: Applications and Industry, 71(5), 225–234. DOI.
- Scargle, J. D.(1981) Studies in astronomical time series analysis I-Modeling random processes in the time domain. The Astrophysical Journal Supplement Series, 45, 1–71.
- Shannon, C. E.(1949) Communication in the Presence of Noise. Proceedings of the IRE, 37(1), 10–21. DOI.
- Smith, J. O.(2007) Introduction to Digital Filters with Audio Applications. . http://www.w3k.org/books/: W3K Publishing
- Stoica, P., & Moses, R. L.(2005) Spectral Analysis of Signals. (1 edition.). Upper Saddle River, N.J: Prentice Hall
- Therrien, C. W.(1992) Discrete random signals and statistical signal processing. . Englewood Cliffs, NJ: Prentice Hall
- Wishnick, A. (2014) Time-Varying Filters for Musical Applications. In DAFx (pp. 69–76).