Linear predictive coding

By Douglas D. O'Shaughnessy. 1988

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Citations in the corpus

Audiodec: An Open-Source Streaming High-Fidelity Neural Audio Codec (2023, neutral): “Most of the conventional parametric codecs [6–8] were built according to in-domain knowledge of psychoacoustics, human speech production systems, and traditional digital signal processing.”
Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding (2019, background): “Therefore, for the subjective quality, we perform MUSHRA tests [31] to show that CMRL with an LPC residual input works better than AMR-WB and OPUS at high bitrates.”
A review of predictive coding algorithms. (2017, background): “The basic idea of linear predictive coding (Makhoul, 1975; O’Shaughnessy, 1988; Vaseghi, 2000) is that each sample of a time series can be approximated as a linear combination of preceding samples, such that:”
End-to-End Binaural Speech Synthesis (2022, background): “Audio codecs have long relied on traditional signal processing and in-domain knowledge of psychoacoustics in order to perform encoding of speech [6] or general audio signals [7, 8].”
Surprisal-Triggered Conditional Computation with Neural Networks (2020, methodology): “In linear predictive coding, a linear ﬁlter is used to predict the next sample of the input signal from previous samples, and the ﬁlter coefﬁcients and error signal are transmitted instead [60, 61, 62].”
Audio Super Resolution using Neural Networks (2017, unknown): “Existing learning-based approaches include Gaussian mixture models (Cheng et al., 1994; Park & Kim, 2000; Pulakka et al., 2011), linear predictive coding (Bradbury, 2000), and neural networks (Li et al., 2015).”
Self-Supervised Speech Representation Learning: A Review (2022, background): “tion [94] and autoregressive language models (LM) for text, where the model learns to predict future information from past.”