Psychoacoustics: Facts and Models
By W. Hartmann. 2001
832 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Speech and Hearing
View Paper: https://www.semanticscholar.org/paper/222a9e771177d4f4025ac8a424da0822be1f43c6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Dong Yu
- Zheng-Hua Tan
- Matti Karjalainen
- Tero Tolonen
- Jeroen Breebaart
- Martin F. McKinney
- Andrew Owens
- Alexei A. Efros
In Computer Vision and Pattern Recognition
- Zheng-Hua Tan
- Meng Yu
- Dong Yu
- Jesper Højvang Jensen
- Matti Karjalainen
- Tero Tolonen
- Shi-Xiong Zhang
- Jeroen Breebaart
In Speech and Hearing
Citations in the corpus
- A computationally efficient multipitch analysis model (2000, methodology): “The only interesting auditory analogy is that the exponent for spectral compression is close to the one used in computation of the loudness density function [ 34 ].”
- An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation (2020, background): “is not uniform, showing a higher discrimination for low frequencies [235]. Human loudness perception is not linear, meaning that the ability to perceive changes in sound level varies with frequency [292]. Masking effects might hinder the perception of weak sounds [71]. The output of PESQ is suppos”
- Compression Artifacts in Perceptual Audio Coding (2008, background): “If the noise is localized close enough to the attacking time, the artifact may not be sensitive to the human hearing due to the pre/post-masking effect [ 34 ].”
- Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring (2010, background): “Such masking effects also occur in human hearing (Zwicker and Fastl, 1999) and can be the cause for calls which are undetectable by human listeners.”
- The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2016, neutral): “[62] alternative to the signal energy.”
- Features for audio and music classification (2003, methodology): “Estimates of these percepts were calculated based on current models ( Zwicker & Fastl, 1990; Daniel & Weber, 1997; Bismarck, 1974).”