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DEEPSIG DATASETS

This repository is a collection of references and software regarding:

DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.

These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.

Please reference the above page or relevant academic papers when using these datasets.

LICENSE NOTICE

All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.

DEEPSIG DATASET: RADIOML 2016.10A

A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.

This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).

Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:

There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:

DEEPSIG DATASET: RADIOML 2016.04C

A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.

This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page

There are three variations within this dataset with the following characteristics and labeling:

Dataset Download: 2016.04C.multisnr.tar.bz2

Docker container

The dockerRML container was used to generate the above dataset.

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DeepSig's dataset for Machine Learning of Software Defined Radio

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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DEEPSIG DATASETS
This repository is a collection of references and software regarding:
DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
Please reference the above page or relevant academic papers when using these datasets.
LICENSE NOTICE
All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.
DEEPSIG DATASET: RADIOML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.
This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).
Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:
There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:
DEEPSIG DATASET: RADIOML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.tar.bz2
Docker container
The dockerRML container was used to generate the above dataset.
About
DeepSig's dataset for Machine Learning of Software Defined Radio 
Resources
Readme
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23 stars
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1 watching
Forks
3 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DEEPSIG DATASETS
This repository is a collection of references and software regarding:
DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
Please reference the above page or relevant academic papers when using these datasets.
LICENSE NOTICE
All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.
DEEPSIG DATASET: RADIOML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.
This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).
Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:
There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:
DEEPSIG DATASET: RADIOML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.tar.bz2
Docker container
The dockerRML container was used to generate the above dataset.
About
DeepSig's dataset for Machine Learning of Software Defined Radio 
Resources
Readme
Activity
Custom properties
Stars
23 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DEEPSIG DATASETS
This repository is a collection of references and software regarding:
DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
Please reference the above page or relevant academic papers when using these datasets.
LICENSE NOTICE
All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.
DEEPSIG DATASET: RADIOML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.
This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).
Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:
There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:
DEEPSIG DATASET: RADIOML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.tar.bz2
Docker container
The dockerRML container was used to generate the above dataset.
About
DeepSig's dataset for Machine Learning of Software Defined Radio 
Resources
Readme
Activity
Custom properties
Stars
23 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
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Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
DEEPSIG DATASETS
This repository is a collection of references and software regarding:
DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
Please reference the above page or relevant academic papers when using these datasets.
LICENSE NOTICE
All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.
DEEPSIG DATASET: RADIOML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.
This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).
Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:
There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:
DEEPSIG DATASET: RADIOML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.tar.bz2
Docker container
The dockerRML container was used to generate the above dataset.
About
DeepSig's dataset for Machine Learning of Software Defined Radio 
Resources
Readme
Activity
Custom properties
Stars
23 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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BranchesTags
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DEEPSIG DATASETS
This repository is a collection of references and software regarding:
DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
Please reference the above page or relevant academic papers when using these datasets.
LICENSE NOTICE
All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.
DEEPSIG DATASET: RADIOML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.
This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).
Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:
There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:
DEEPSIG DATASET: RADIOML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.tar.bz2
Docker container
The dockerRML container was used to generate the above dataset.
About
DeepSig's dataset for Machine Learning of Software Defined Radio 
Resources
Readme
Activity
Custom properties
Stars
23 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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BranchesTags
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DEEPSIG DATASETS
This repository is a collection of references and software regarding:
DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
Please reference the above page or relevant academic papers when using these datasets.
LICENSE NOTICE
All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.
DEEPSIG DATASET: RADIOML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.
This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).
Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:
There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:
DEEPSIG DATASET: RADIOML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.tar.bz2
Docker container
The dockerRML container was used to generate the above dataset.
About
DeepSig's dataset for Machine Learning of Software Defined Radio 
Resources
Readme
Activity
Custom properties
Stars
23 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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DEEPSIG DATASETS
This repository is a collection of references and software regarding:
DeepSig has created a small corpus of standard datasets which can be used for original and reproducible research, experimentation, measurement and comparison by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
Please reference the above page or relevant academic papers when using these datasets.
LICENSE NOTICE
All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). If an alternative license is needed, please contact info@deepsig.io.
DEEPSIG DATASET: RADIOML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.
This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).
Note: That example ClassifierJupyter notebook requires some fixes from a fork to run successfully. Those are merged here:
There is also another fork with updated Keras 2 and Theanos that claims 84% accuracy:
DEEPSIG DATASET: RADIOML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on the DeepSig Publications Page
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.tar.bz2
Docker container
The dockerRML container was used to generate the above dataset.
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DeepSig's dataset for Machine Learning of Software Defined Radio 
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