modified version of a published matlab function Search Results


matlab toolbox cftool  (MathWorks Inc)
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matlab simulink modeling tool  (MathWorks Inc)
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matlab global optimization toolbox  (MathWorks Inc)
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hartmann 1997 wide filter bands  (MathWorks Inc)
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FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
Hartmann 1997 Wide Filter Bands, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab toolbox  (MathWorks Inc)
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MathWorks Inc matlab toolbox
FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
Matlab Toolbox, 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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custommade software routine  (MathWorks Inc)
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FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
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heart-lung machine  (LivaNova Inc)
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LivaNova Inc heart-lung machine
FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
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q8 data acquisition board  (Quanser Consulting)
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FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
Q8 Data Acquisition Board, supplied by Quanser Consulting, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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code building tool  (MathWorks Inc)
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FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
Code Building Tool, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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tds 2024b oscilloscope  (Tektronix inc)
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FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
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linear analysis toolbox lat  (MathWorks Inc)
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FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no <t>gammatone</t> filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.
Linear Analysis Toolbox Lat, 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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Image Search Results


FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no gammatone filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.

Journal: The Journal of the Acoustical Society of America

Article Title: Randomizing spectral cues used to resolve front-back reversals in sound-source localization.

doi: 10.1121/10.0020563

Figure Lengend Snippet: FIG. 2. Spectra of the low-frequency [(A), (B), (D), and (F)] and high-frequency [(C), (E), and (G)] filtered noises, with (A)–(C) indicating the spectra when there was no gammatone filtering (0-dB level change), (D) and (E) when the random-level change was 10 dB (10-dB level change), and (F) and (G) when the random-level change was 20 dB (20-dB level change). The spectrum shown in (A) is for a 200-ms filtered noise, and in all other panels [(B)–(G)], the duration was 2500 ms. The spectra are only examples, as the spectra varied randomly due to the random variation in the level of each gammatone filter. In each case, the amplitudes shown in the figure are scaled to that of the maximum amplitude for the particular noise sample.

Article Snippet: In all other cases, the spectrum of each noise was divided into a series of successive, non-overlapping 1-Cam [equivalent rectangular bandwidth (ERB); see Moore and Glasberg (1983) and Hartmann (1997)] wide filter bands (based on implementation of a gammatone filter bank in MATLAB’s Audio Toolbox).

Techniques: