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patterson–holdsworth auditory filter bank apple computer technical report no. 35  (apple inc)

 
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    apple inc patterson–holdsworth auditory filter bank apple computer technical report no. 35
    Patterson–Holdsworth Auditory Filter Bank Apple Computer Technical Report No. 35, supplied by apple inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/patterson%E2%80%93holdsworth+auditory+filter+bank/patterson+holdsworth+auditory+filter+bank/pmc08582905-1062-5-9
    Average 90 stars, based on 1 article reviews
    patterson–holdsworth auditory filter bank apple computer technical report no. 35 - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: An approximate transfer function for the dual-resonance nonlinear filter model of auditory frequency selectivity.
    Article Snippet: The dual-resonance nonlinear filter @Meddis et al., J. Acoust.. Soc.. Am.

    Article Title: Efficient coding in human auditory perception
    Article Snippet: “ An efficient implementation of the Patterson–Holdsworth auditory filter bank ,” Technical Report No. 35, Apple Computer, Cupertino, CA.

    Article Title: Predicting Perceived Visual and Cognitive Distractions of Drivers With Multimodal Features
    Article Snippet: A driver’s behaviors can be affected by visual, cognitive, auditory, and manual distractions.. While it is important to identify the patterns associated with particular secondary tasks, it is more general and useful to define distraction modes that capture the general behaviors induced by various sources of distractions.. By explicitly modeling the distinction between types of distractions, we can assess the detrimental effects induced by new in-vehicle technology.

    Article Title: Narrow-band autocorrelation function features for the automatic recognition of acoustic environments.
    Article Snippet: Acoustic environments are typically composed of multiple sound sources of different typologies, making them especially complex to model and parameterize.. To develop an automatic acoustic environment recognition system, this work proposes a spectro-temporal signal parameterization technique inspired by human perception.. The proposed parameters are derived from the analysis of the autocorrelation function of narrow-band signals (NB-ACF) obtained from an auditory gammatone filter bank.



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