slaney’s matlab toolbox Search Results


auditory toolbox  (MathWorks Inc)
90
MathWorks Inc auditory toolbox
Auditory Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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slaney's auditory toolbox 2.0 for  (MathWorks Inc)
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MathWorks Inc slaney's auditory toolbox 2.0 for
Slaney's Auditory Toolbox 2.0 For, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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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 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab auditory toolbox  (MathWorks Inc)
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Matlab Auditory Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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slaney's matlab auditory toolbox  (MathWorks Inc)
90
MathWorks Inc slaney's matlab auditory toolbox
Slaney's Matlab Auditory Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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slaneys matlab auditory toolbox  (MathWorks Inc)
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MathWorks Inc slaneys matlab auditory toolbox
Slaneys Matlab Auditory Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gammatone filterbank  (MathWorks Inc)
90
MathWorks Inc gammatone filterbank
Gammatone Filterbank, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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voicebox for matlab  (MathWorks Inc)
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slaney's matlab toolbox for the gammatone filterbank  (MathWorks Inc)
90
MathWorks Inc slaney's matlab toolbox for the gammatone filterbank
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.
Slaney's Matlab Toolbox For The Gammatone Filterbank, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab 8.0  (MathWorks Inc)
90
MathWorks Inc matlab 8.0
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.
Matlab 8.0, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


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: