Features Based on Auditory Physiology and Perception

25 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (2021, background): “The first one is feature-based, which focuses on creating robust features [9, 10].”
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition (2018, background): “Alternatively, robustness can also be achieved by training using features that are domain invariant [10, 11, 12, 13, 14].”
An Overview of Noise-Robust Automatic Speech Recognition (2014, background): “We hope that despite the limited scope the reader will ﬁ nd this overview useful.”
Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition (2016, background): “…many of the attributes of PNCC processing have been strongly influenced by consideration of various attributes of human auditory processing (cf. [35], [36]), we have favored approaches that provide pragmatic gains in robustness at small computational cost over approaches that are more faithful…”
Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification (2017, methodology): “Such handcrafted features rely on the simplified auditory models [15].”