Discrete-time Signal Processing

Williamson, D.

doi:10.1007/978-1-4471-0541-1

Cited by 93 publications

(127 citation statements)

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“…Spectrogram-based approaches rely on a time-frequency representation obtained through short-term Fourier Transform (Williamson, 2012). Considering the input mixture spectrogram, these approaches aim to assign each timefrequency bin (TFB) to one of the sources.…”

Section: Related Workmentioning

confidence: 99%

Wavesplit: End-to-End Speech Separation by Speaker Clustering

Zeghidour¹,

Grangier²

2020

Preprint

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We introduce Wavesplit, an end-to-end speech separation system. From a single recording of mixed speech, the model infers and clusters representations of each speaker and then estimates each source signal conditioned on the inferred representations. The model is trained on the raw waveform to jointly perform the two tasks. Our model infers a set of speaker representations through clustering, which addresses the fundamental permutation problem of speech separation. Moreover, the sequence-wide speaker representations provide a more robust separation of long, challenging sequences, compared to previous approaches. We show that Wavesplit outperforms the previous state-of-the-art on clean mixtures of 2 or 3 speakers (WSJ0-2mix, WSJ0-3mix), as well as in noisy (WHAM!) and reverberated (WHAMR!) conditions. As an additional contribution, we further improve our model by introducing online data augmentation for separation.

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Section: Related Workmentioning

confidence: 99%

Wavesplit: End-to-End Speech Separation by Speaker Clustering

Zeghidour¹,

Grangier²

2020

Preprint

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“…Excellent references with a focus on deterministic signals are Oppenheim et al (1996); Ljung and Glad (1994); Hsu (2013); Hayes (2011), while Smith (2007b,a,c) provides a comprehensive treatment of Fourier analysis and filters. A gentle introduction to stochastic signals is provided by Kay (1993); Williamson (1999); Oppenheim (2015) while Wang (2009) provides an excellent review of orthogonal transforms.…”

Section: Statisticsmentioning

confidence: 99%

Statistical Treatment, Fourier and Modal Decomposition

Mendez¹

2022

Preprint

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“…8 Discrete time modeling uses sample "points of time," which are separate signals equivalently unchanged throughout each non-zero region of time ("time period")-that is, time is viewed as a discrete variable that can be assigned. 9 Scaling of sample or frame periods creates uniform symmetric counts, or parts of a whole. The whole and parts can be converted into the modern SI unit for time: the second.…”

Section: Introductionmentioning

confidence: 99%

Dynamical astronomical object-relative cyclic signal inputs for discrete time modeling with calendric applications

Moore¹

2023

Preprint

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Accumulation of temporal errors occurs in continuous time modeling based on dynamical astronomical cycles; however, cyclic differences can also be advantageous. Dynamical cycles in nature, especially astronomical cycles, vary and lack uniformity. Unifying discrete time modeling with continuous time can chart these differences throughout linear time, building a reference repository. Ancient timekeeping systems are combined with modern computation and set theories, and when integrated with a signal system, cyclic fundamental temporal elements are created. Uniform scaling elements create unique and uniform cyclic and object-relative natural number units of time, each convertible to SI unit seconds. Despite countless Julian calendar-based comparison studies on timekeeping, ancient methodologies remain a mystery. This approach opens a hypothesized methodology that is testable for ancient observational calendric timekeeping systems. We present the first known pairing function to synchronize and assemble various independent object-relative observational-based astronomical cycles and their temporal elements. The model is applied to test a comparison between a previously proposed calendar model derived from Neolithic China and the Mesoamerican timekeeping divisional system. A 13-lunation calendar year built upon a 20-year cycle is introduced as the first new hypothesized astronomical cyclic pairing for Mesoamerica in nearly 500 years. Results validate a hypothesized link between Mesoamerica and ancient China. Combining mathematical and computational methodologies, our cyclic object-relative signal inputs from continuous time can yield new applications in astrometry and physics, as well as tools for archaeological research.

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Discrete-time Signal Processing

Abstract: British Library Cataloguing in Publication Data Williarnson, Darrell, 1948-Discrete-time signal processing. -(Advanced textbooks in control and signal processing) I.Signal processing 2.Discrete-time systems I.Tide 621.3'82:2

Cited by 93 publications

References 0 publications

Wavesplit: End-to-End Speech Separation by Speaker Clustering

Wavesplit: End-to-End Speech Separation by Speaker Clustering

Statistical Treatment, Fourier and Modal Decomposition

Dynamical astronomical object-relative cyclic signal inputs for discrete time modeling with calendric applications

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