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slaney s matlab auditory toolbox slaney  (MathWorks Inc)


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    MathWorks Inc slaney s matlab auditory toolbox slaney
    Slaney S Matlab Auditory Toolbox Slaney, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2340 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/slaney+matlab+toolbox/Database+Toolbox/pmc06546930-173-26-27
    Average 96 stars, based on 2340 article reviews
    slaney s matlab auditory toolbox slaney - by Bioz Stars, 2026-10
    96/100 stars

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    other:

    Article Title: Why Middle-Aged Listeners Have Trouble Hearing in Everyday Settings
    Article Snippet: We used the Slaney MATLAB toolbox [ 42 ] (functions ERBSpace, MakeERBFilters, and ERBFilterBank) to simulate the signals driving 40 auditory nerve fibers (100–12,000 Hz).



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    Simple spiking model of the auditory periphery . The cochlea and inner hair cells are modeled using <t>gammatone</t> filtering followed by half-wave rectification and 1/3-power law compression. We model the auditory nerve fibers as leaky integrate-and-fire neurons defined by the stochastic differential equation τ d V d t = I − V + k ξ ( t ) where the current I is the output of the inner hair cell model and J( t ) is physiological white noise (not the acoustic input noise). (A) Python implementation with the Brian Hears toolbox. Variable I in the neuron model is linked to the output of the <t>filterbank.</t> (B) Raster plot of the model output.
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    Simple spiking model of the auditory periphery . The cochlea and inner hair cells are modeled using <t>gammatone</t> filtering followed by half-wave rectification and 1/3-power law compression. We model the auditory nerve fibers as leaky integrate-and-fire neurons defined by the stochastic differential equation τ d V d t = I − V + k ξ ( t ) where the current I is the output of the inner hair cell model and J( t ) is physiological white noise (not the acoustic input noise). (A) Python implementation with the Brian Hears toolbox. Variable I in the neuron model is linked to the output of the <t>filterbank.</t> (B) Raster plot of the model output.
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    Simple spiking model of the auditory periphery . The cochlea and inner hair cells are modeled using gammatone filtering followed by half-wave rectification and 1/3-power law compression. We model the auditory nerve fibers as leaky integrate-and-fire neurons defined by the stochastic differential equation τ d V d t = I − V + k ξ ( t ) where the current I is the output of the inner hair cell model and J( t ) is physiological white noise (not the acoustic input noise). (A) Python implementation with the Brian Hears toolbox. Variable I in the neuron model is linked to the output of the filterbank. (B) Raster plot of the model output.

    Journal: Frontiers in Neuroinformatics

    Article Title: Brian Hears: Online Auditory Processing Using Vectorization Over Channels

    doi: 10.3389/fninf.2011.00009

    Figure Lengend Snippet: Simple spiking model of the auditory periphery . The cochlea and inner hair cells are modeled using gammatone filtering followed by half-wave rectification and 1/3-power law compression. We model the auditory nerve fibers as leaky integrate-and-fire neurons defined by the stochastic differential equation τ d V d t = I − V + k ξ ( t ) where the current I is the output of the inner hair cell model and J( t ) is physiological white noise (not the acoustic input noise). (A) Python implementation with the Brian Hears toolbox. Variable I in the neuron model is linked to the output of the filterbank. (B) Raster plot of the model output.

    Article Snippet: We compared them with non-vectorized implementations taken from existing toolboxes: Slaney's Matlab toolbox for the gammatone filterbank (Slaney, ), and Meddis's Matlab toolbox for the DRNL (Meddis, ), which we improved with respect to memory allocation to allow a fair comparison.

    Techniques:

    Computation time taken to simulate a gammatone filterbank as a function of the number of channels, with a 200-ms sound at 20 kHz [(B) is a magnified version of (A)] . Five different implementations are compared: Brian Hears in pure Python with vectorization over channels (BH Python), Brian Hears with C code generation, sequential channels (BH C1) or vectorized channels (BH C2), Brian Hears with GPU code generation, and Matlab with operations on time-indexed arrays.

    Journal: Frontiers in Neuroinformatics

    Article Title: Brian Hears: Online Auditory Processing Using Vectorization Over Channels

    doi: 10.3389/fninf.2011.00009

    Figure Lengend Snippet: Computation time taken to simulate a gammatone filterbank as a function of the number of channels, with a 200-ms sound at 20 kHz [(B) is a magnified version of (A)] . Five different implementations are compared: Brian Hears in pure Python with vectorization over channels (BH Python), Brian Hears with C code generation, sequential channels (BH C1) or vectorized channels (BH C2), Brian Hears with GPU code generation, and Matlab with operations on time-indexed arrays.

    Article Snippet: We compared them with non-vectorized implementations taken from existing toolboxes: Slaney's Matlab toolbox for the gammatone filterbank (Slaney, ), and Meddis's Matlab toolbox for the DRNL (Meddis, ), which we improved with respect to memory allocation to allow a fair comparison.

    Techniques:

    Computation time taken to simulate a DRNL filterbank as a function of the number of channels, with the same algorithms as in Figure [(B) is a magnified version of (A)] .

    Journal: Frontiers in Neuroinformatics

    Article Title: Brian Hears: Online Auditory Processing Using Vectorization Over Channels

    doi: 10.3389/fninf.2011.00009

    Figure Lengend Snippet: Computation time taken to simulate a DRNL filterbank as a function of the number of channels, with the same algorithms as in Figure [(B) is a magnified version of (A)] .

    Article Snippet: We compared them with non-vectorized implementations taken from existing toolboxes: Slaney's Matlab toolbox for the gammatone filterbank (Slaney, ), and Meddis's Matlab toolbox for the DRNL (Meddis, ), which we improved with respect to memory allocation to allow a fair comparison.

    Techniques:

    Auditory model with feedback . (A) Python program defining a time-varying filterbank with center frequency modulated by the output of a low-pass filter using the Brian Hears toolbox. (B) Corresponding box representation.

    Journal: Frontiers in Neuroinformatics

    Article Title: Brian Hears: Online Auditory Processing Using Vectorization Over Channels

    doi: 10.3389/fninf.2011.00009

    Figure Lengend Snippet: Auditory model with feedback . (A) Python program defining a time-varying filterbank with center frequency modulated by the output of a low-pass filter using the Brian Hears toolbox. (B) Corresponding box representation.

    Article Snippet: We compared them with non-vectorized implementations taken from existing toolboxes: Slaney's Matlab toolbox for the gammatone filterbank (Slaney, ), and Meddis's Matlab toolbox for the DRNL (Meddis, ), which we improved with respect to memory allocation to allow a fair comparison.

    Techniques:

    Vectorization over frequency and multiple head related transfer functions (HRTFs) . (A) Schematic of nested vectorization over multiple HRTFs and frequencies. (B) Corresponding Python code. (C) Stereo output of the filterbank for four HRTF pairs.

    Journal: Frontiers in Neuroinformatics

    Article Title: Brian Hears: Online Auditory Processing Using Vectorization Over Channels

    doi: 10.3389/fninf.2011.00009

    Figure Lengend Snippet: Vectorization over frequency and multiple head related transfer functions (HRTFs) . (A) Schematic of nested vectorization over multiple HRTFs and frequencies. (B) Corresponding Python code. (C) Stereo output of the filterbank for four HRTF pairs.

    Article Snippet: We compared them with non-vectorized implementations taken from existing toolboxes: Slaney's Matlab toolbox for the gammatone filterbank (Slaney, ), and Meddis's Matlab toolbox for the DRNL (Meddis, ), which we improved with respect to memory allocation to allow a fair comparison.

    Techniques: