Linear prediction: A tutorial review

By John I. Makhoul. 1975

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Citations in the corpus

LPCNET: Improving Neural Speech Synthesis through Linear Prediction (2018, background): “Although some of these are indeed very hard to model, we know that the vocal tract response can be represented reasonably well by a simple all-pole linear ﬁlter [16].”
Seizure prediction: the long and winding road. (2007, methodology): “Identification of an appropriate ARMA model allows the design of special filters, forecasting time series or estimation of the power spectrum and derived measures such as the so-called transfer function (Makhoul, 1973; Lopes da Silva, 1987; Lopes da Silva and Mars, 1987).”
Text-to-Speech Synthesis (2009, methodology): “Because the square matrix φ s symmetric a much faster technique known asCholskey decompositioncan be used [302] [295].”
Compression Artifacts in Perceptual Audio Coding (2008, background): “2(b), results from cutting the high-frequency (HF) content during audio compression, possibly leading to “muffled” audio.”
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders (2019, background): “, 2002; Livescu and Glass, 2004) or other spectral transforms (Zwicker, 1961; Makhoul, 1975; Hermansky, 1990; Hermansky et al., 1991; Coifman and Wickerhauser, 1992; Shao et al., 2009) — have been proposed as alternatives to MFCCs.”
Introduction to Digital Speech Processing (2007, methodology): “That this is true in general can be argued using the Parseval theorem of Fourier analysis [74].”
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2016, unknown): “are computed from the roots of Linear Predictor (LP) [67]”