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openSMILE (open-source Speech and Music Interpretation by Large-space Extraction) is a complete and open-source toolkit for audio analysis, processing and classification especially targeted at speech and music applications, e.g. automatic speech recognition, speaker identification, emotion recognition, or beat tracking and chord detection.

It is written purely in C++, has a fast, efficient, and flexible architecture, and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS and Raspberry Pi.

See also the standalone opensmile Python package for an easy-to-use wrapper if you are working in Python.

What's new

Please see our blog post on audeering.com for a summary of the new features in version 3.0.

Quick start

Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.

For more details on how to customize builds, build for other platforms and use openSMILE, see Section Get started in the documentation.

Linux/MacOS

Prerequisites:

  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.

Build files will be generated in the ./build subdirectory. You can find the main SMILExtract binary in ./build/progsrc/smilextract.

Windows

Prerequisites:

  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.

Build files will be generated in the ./build subdirectory. You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.

Documentation

You can find extensive documentation with step-by-step instructions on how to build openSMILE and get started at https://audeering.github.io/opensmile/.

History

The toolkit was first developed at the Institute for Human-Machine Communication at the Technische Universität München in Munich, Germany. It was started within the SEMAINE EU-FP7 research project. The toolkit is now owned and maintained by audEERING GmbH, who provide intelligent audio analysis solutions, automatic speech emotion recognition, and paralinguistic speech analysis software packages as well as consulting and development services on these topics.

Contributing and Support

We welcome contributions! For feedback and technical support, please use the issue tracker.

Licensing

openSMILE follows a dual-licensing model. Since the main goal of the project is a widespread use of the software to facilitate research in the field of machine learning from audio-visual signals, the source code and binaries are freely available for private, research, and educational use under an open-source license (see LICENSE). It is not allowed to use the open-source version of openSMILE for any sort of commercial product. Fundamental research in companies, for example, is permitted, but if a product is the result of the research, we require you to buy a commercial development license. Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.

Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller
Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany
Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)
Copyright © 2016-2020, audEERING GmbH

Citing

Please cite openSMILE in your publications by citing the following paper:

Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.

About

The Munich Open-Source Large-Scale Multimedia Feature Extractor

audeering.github.io/opensmile/

Resources

Readme
License
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - drux007/opensmile: The Munich Open-Source Large-Scale Multimedia Feature Extractor · GitHub
Skip to content
BranchesTags
Repository files navigation
Latest releaseLatest release dateAll releasesDocumentation
openSMILE (open-source Speech and Music Interpretation by Large-space Extraction)
is a complete and open-source toolkit for audio analysis,
processing and classification especially targeted at speech and
music applications, e.g. automatic speech recognition, speaker identification,
emotion recognition, or beat tracking and chord detection.
It is written purely in C++, has a fast, efficient, and flexible architecture,
and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS
and Raspberry Pi.
See also the standalone
opensmile Python package
for an easy-to-use wrapper if you are working in Python.
What's new
Please see our blog post on audeering.com for a summary of the new features in version 3.0.
Quick start
Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.
For more details on how to customize builds, build for other platforms and use
openSMILE, see Section Get started
in the documentation.
Linux/MacOS
Prerequisites:
  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract binary in ./build/progsrc/smilextract.
Windows
Prerequisites:
  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.
Documentation
You can find extensive documentation with step-by-step instructions on how to build
openSMILE and get started at https://audeering.github.io/opensmile/.
History
The toolkit was first developed at the Institute for Human-Machine
Communication at the Technische Universität München in Munich, Germany.
It was started within the SEMAINE EU-FP7 research project.
The toolkit is now owned and maintained
by audEERING GmbH, who provide intelligent
audio analysis solutions, automatic speech emotion recognition,
and paralinguistic speech analysis software packages as well as
consulting and development services on these topics.
Contributing and Support
We welcome contributions! For feedback and technical support,
please use the issue tracker.
Licensing
openSMILE follows a dual-licensing model. Since the main goal of the project
is a widespread use of the software to facilitate research in the field of
machine learning from audio-visual signals, the source code and binaries are
freely available for private, research, and educational use under an open-source license
(see LICENSE).
It is not allowed to use the open-source version of openSMILE for any sort of commercial product.
Fundamental research in companies, for example, is permitted, but if a product is the result of
the research, we require you to buy a commercial development license.
Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.
Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller

Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany

Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)

Copyright © 2016-2020, audEERING GmbH
Citing
Please cite openSMILE in your publications by citing the following paper:
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
About
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering.github.io/opensmile/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - drux007/opensmile: The Munich Open-Source Large-Scale Multimedia Feature Extractor · GitHub
Skip to content
BranchesTags
Repository files navigation
Latest releaseLatest release dateAll releasesDocumentation
openSMILE (open-source Speech and Music Interpretation by Large-space Extraction)
is a complete and open-source toolkit for audio analysis,
processing and classification especially targeted at speech and
music applications, e.g. automatic speech recognition, speaker identification,
emotion recognition, or beat tracking and chord detection.
It is written purely in C++, has a fast, efficient, and flexible architecture,
and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS
and Raspberry Pi.
See also the standalone
opensmile Python package
for an easy-to-use wrapper if you are working in Python.
What's new
Please see our blog post on audeering.com for a summary of the new features in version 3.0.
Quick start
Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.
For more details on how to customize builds, build for other platforms and use
openSMILE, see Section Get started
in the documentation.
Linux/MacOS
Prerequisites:
  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract binary in ./build/progsrc/smilextract.
Windows
Prerequisites:
  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.
Documentation
You can find extensive documentation with step-by-step instructions on how to build
openSMILE and get started at https://audeering.github.io/opensmile/.
History
The toolkit was first developed at the Institute for Human-Machine
Communication at the Technische Universität München in Munich, Germany.
It was started within the SEMAINE EU-FP7 research project.
The toolkit is now owned and maintained
by audEERING GmbH, who provide intelligent
audio analysis solutions, automatic speech emotion recognition,
and paralinguistic speech analysis software packages as well as
consulting and development services on these topics.
Contributing and Support
We welcome contributions! For feedback and technical support,
please use the issue tracker.
Licensing
openSMILE follows a dual-licensing model. Since the main goal of the project
is a widespread use of the software to facilitate research in the field of
machine learning from audio-visual signals, the source code and binaries are
freely available for private, research, and educational use under an open-source license
(see LICENSE).
It is not allowed to use the open-source version of openSMILE for any sort of commercial product.
Fundamental research in companies, for example, is permitted, but if a product is the result of
the research, we require you to buy a commercial development license.
Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.
Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller

Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany

Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)

Copyright © 2016-2020, audEERING GmbH
Citing
Please cite openSMILE in your publications by citing the following paper:
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
About
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering.github.io/opensmile/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
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style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - drux007/opensmile: The Munich Open-Source Large-Scale Multimedia Feature Extractor · GitHub
Skip to content
BranchesTags
Repository files navigation
Latest releaseLatest release dateAll releasesDocumentation
openSMILE (open-source Speech and Music Interpretation by Large-space Extraction)
is a complete and open-source toolkit for audio analysis,
processing and classification especially targeted at speech and
music applications, e.g. automatic speech recognition, speaker identification,
emotion recognition, or beat tracking and chord detection.
It is written purely in C++, has a fast, efficient, and flexible architecture,
and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS
and Raspberry Pi.
See also the standalone
opensmile Python package
for an easy-to-use wrapper if you are working in Python.
What's new
Please see our blog post on audeering.com for a summary of the new features in version 3.0.
Quick start
Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.
For more details on how to customize builds, build for other platforms and use
openSMILE, see Section Get started
in the documentation.
Linux/MacOS
Prerequisites:
  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract binary in ./build/progsrc/smilextract.
Windows
Prerequisites:
  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.
Documentation
You can find extensive documentation with step-by-step instructions on how to build
openSMILE and get started at https://audeering.github.io/opensmile/.
History
The toolkit was first developed at the Institute for Human-Machine
Communication at the Technische Universität München in Munich, Germany.
It was started within the SEMAINE EU-FP7 research project.
The toolkit is now owned and maintained
by audEERING GmbH, who provide intelligent
audio analysis solutions, automatic speech emotion recognition,
and paralinguistic speech analysis software packages as well as
consulting and development services on these topics.
Contributing and Support
We welcome contributions! For feedback and technical support,
please use the issue tracker.
Licensing
openSMILE follows a dual-licensing model. Since the main goal of the project
is a widespread use of the software to facilitate research in the field of
machine learning from audio-visual signals, the source code and binaries are
freely available for private, research, and educational use under an open-source license
(see LICENSE).
It is not allowed to use the open-source version of openSMILE for any sort of commercial product.
Fundamental research in companies, for example, is permitted, but if a product is the result of
the research, we require you to buy a commercial development license.
Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.
Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller

Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany

Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)

Copyright © 2016-2020, audEERING GmbH
Citing
Please cite openSMILE in your publications by citing the following paper:
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
About
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering.github.io/opensmile/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
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observer.observe(document.body, { childList: true, subtree: true });
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - drux007/opensmile: The Munich Open-Source Large-Scale Multimedia Feature Extractor · GitHub
Skip to content
BranchesTags
Repository files navigation
Latest releaseLatest release dateAll releasesDocumentation
openSMILE (open-source Speech and Music Interpretation by Large-space Extraction)
is a complete and open-source toolkit for audio analysis,
processing and classification especially targeted at speech and
music applications, e.g. automatic speech recognition, speaker identification,
emotion recognition, or beat tracking and chord detection.
It is written purely in C++, has a fast, efficient, and flexible architecture,
and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS
and Raspberry Pi.
See also the standalone
opensmile Python package
for an easy-to-use wrapper if you are working in Python.
What's new
Please see our blog post on audeering.com for a summary of the new features in version 3.0.
Quick start
Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.
For more details on how to customize builds, build for other platforms and use
openSMILE, see Section Get started
in the documentation.
Linux/MacOS
Prerequisites:
  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract binary in ./build/progsrc/smilextract.
Windows
Prerequisites:
  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.
Documentation
You can find extensive documentation with step-by-step instructions on how to build
openSMILE and get started at https://audeering.github.io/opensmile/.
History
The toolkit was first developed at the Institute for Human-Machine
Communication at the Technische Universität München in Munich, Germany.
It was started within the SEMAINE EU-FP7 research project.
The toolkit is now owned and maintained
by audEERING GmbH, who provide intelligent
audio analysis solutions, automatic speech emotion recognition,
and paralinguistic speech analysis software packages as well as
consulting and development services on these topics.
Contributing and Support
We welcome contributions! For feedback and technical support,
please use the issue tracker.
Licensing
openSMILE follows a dual-licensing model. Since the main goal of the project
is a widespread use of the software to facilitate research in the field of
machine learning from audio-visual signals, the source code and binaries are
freely available for private, research, and educational use under an open-source license
(see LICENSE).
It is not allowed to use the open-source version of openSMILE for any sort of commercial product.
Fundamental research in companies, for example, is permitted, but if a product is the result of
the research, we require you to buy a commercial development license.
Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.
Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller

Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany

Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)

Copyright © 2016-2020, audEERING GmbH
Citing
Please cite openSMILE in your publications by citing the following paper:
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
About
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering.github.io/opensmile/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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function tryTheater() {
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - drux007/opensmile: The Munich Open-Source Large-Scale Multimedia Feature Extractor · GitHub
Skip to content
BranchesTags
Repository files navigation
Latest releaseLatest release dateAll releasesDocumentation
openSMILE (open-source Speech and Music Interpretation by Large-space Extraction)
is a complete and open-source toolkit for audio analysis,
processing and classification especially targeted at speech and
music applications, e.g. automatic speech recognition, speaker identification,
emotion recognition, or beat tracking and chord detection.
It is written purely in C++, has a fast, efficient, and flexible architecture,
and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS
and Raspberry Pi.
See also the standalone
opensmile Python package
for an easy-to-use wrapper if you are working in Python.
What's new
Please see our blog post on audeering.com for a summary of the new features in version 3.0.
Quick start
Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.
For more details on how to customize builds, build for other platforms and use
openSMILE, see Section Get started
in the documentation.
Linux/MacOS
Prerequisites:
  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract binary in ./build/progsrc/smilextract.
Windows
Prerequisites:
  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.
Documentation
You can find extensive documentation with step-by-step instructions on how to build
openSMILE and get started at https://audeering.github.io/opensmile/.
History
The toolkit was first developed at the Institute for Human-Machine
Communication at the Technische Universität München in Munich, Germany.
It was started within the SEMAINE EU-FP7 research project.
The toolkit is now owned and maintained
by audEERING GmbH, who provide intelligent
audio analysis solutions, automatic speech emotion recognition,
and paralinguistic speech analysis software packages as well as
consulting and development services on these topics.
Contributing and Support
We welcome contributions! For feedback and technical support,
please use the issue tracker.
Licensing
openSMILE follows a dual-licensing model. Since the main goal of the project
is a widespread use of the software to facilitate research in the field of
machine learning from audio-visual signals, the source code and binaries are
freely available for private, research, and educational use under an open-source license
(see LICENSE).
It is not allowed to use the open-source version of openSMILE for any sort of commercial product.
Fundamental research in companies, for example, is permitted, but if a product is the result of
the research, we require you to buy a commercial development license.
Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.
Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller

Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany

Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)

Copyright © 2016-2020, audEERING GmbH
Citing
Please cite openSMILE in your publications by citing the following paper:
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
About
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering.github.io/opensmile/
Resources
Readme
License
Activity
Stars
0 stars
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0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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el.style.top = 'auto';
el.style.zIndex = 'auto';
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unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - drux007/opensmile: The Munich Open-Source Large-Scale Multimedia Feature Extractor · GitHub
Skip to content
BranchesTags
Repository files navigation
Latest releaseLatest release dateAll releasesDocumentation
openSMILE (open-source Speech and Music Interpretation by Large-space Extraction)
is a complete and open-source toolkit for audio analysis,
processing and classification especially targeted at speech and
music applications, e.g. automatic speech recognition, speaker identification,
emotion recognition, or beat tracking and chord detection.
It is written purely in C++, has a fast, efficient, and flexible architecture,
and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS
and Raspberry Pi.
See also the standalone
opensmile Python package
for an easy-to-use wrapper if you are working in Python.
What's new
Please see our blog post on audeering.com for a summary of the new features in version 3.0.
Quick start
Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.
For more details on how to customize builds, build for other platforms and use
openSMILE, see Section Get started
in the documentation.
Linux/MacOS
Prerequisites:
  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract binary in ./build/progsrc/smilextract.
Windows
Prerequisites:
  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.
Documentation
You can find extensive documentation with step-by-step instructions on how to build
openSMILE and get started at https://audeering.github.io/opensmile/.
History
The toolkit was first developed at the Institute for Human-Machine
Communication at the Technische Universität München in Munich, Germany.
It was started within the SEMAINE EU-FP7 research project.
The toolkit is now owned and maintained
by audEERING GmbH, who provide intelligent
audio analysis solutions, automatic speech emotion recognition,
and paralinguistic speech analysis software packages as well as
consulting and development services on these topics.
Contributing and Support
We welcome contributions! For feedback and technical support,
please use the issue tracker.
Licensing
openSMILE follows a dual-licensing model. Since the main goal of the project
is a widespread use of the software to facilitate research in the field of
machine learning from audio-visual signals, the source code and binaries are
freely available for private, research, and educational use under an open-source license
(see LICENSE).
It is not allowed to use the open-source version of openSMILE for any sort of commercial product.
Fundamental research in companies, for example, is permitted, but if a product is the result of
the research, we require you to buy a commercial development license.
Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.
Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller

Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany

Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)

Copyright © 2016-2020, audEERING GmbH
Citing
Please cite openSMILE in your publications by citing the following paper:
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
About
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering.github.io/opensmile/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
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var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - drux007/opensmile: The Munich Open-Source Large-Scale Multimedia Feature Extractor · GitHub
Skip to content
BranchesTags
Repository files navigation
Latest releaseLatest release dateAll releasesDocumentation
openSMILE (open-source Speech and Music Interpretation by Large-space Extraction)
is a complete and open-source toolkit for audio analysis,
processing and classification especially targeted at speech and
music applications, e.g. automatic speech recognition, speaker identification,
emotion recognition, or beat tracking and chord detection.
It is written purely in C++, has a fast, efficient, and flexible architecture,
and runs on desktop, mobile and embedded platforms such as Linux, Windows, MacOS, Android, iOS
and Raspberry Pi.
See also the standalone
opensmile Python package
for an easy-to-use wrapper if you are working in Python.
What's new
Please see our blog post on audeering.com for a summary of the new features in version 3.0.
Quick start
Pre-built x64 binaries for Windows, Linux and macOS are provided on the Releases page. Alternatively, you may follow the steps below to build openSMILE yourself, if desired.
For more details on how to customize builds, build for other platforms and use
openSMILE, see Section Get started
in the documentation.
Linux/MacOS
Prerequisites:
  • A version of gcc and g++ or Clang need to be installed that supports C++11.
  • CMake 3.5.1 or later needs to be installed and in the PATH.
  1. In build_flags.sh, set build flags and options as desired.
  2. Run bash build.sh.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract binary in ./build/progsrc/smilextract.
Windows
Prerequisites:
  • Visual Studio 2017 or higher with C++ components is required.
  • CMake 3.15 or later needs to be installed and in the PATH.
  1. In build_flags.ps1, set build flags and options as desired.
  2. Run powershell -ExecutionPolicy Bypass -File build.ps1.
Build files will be generated in the ./build subdirectory.
You can find the main SMILExtract.exe binary in ./build/progsrc/smilextract.
Documentation
You can find extensive documentation with step-by-step instructions on how to build
openSMILE and get started at https://audeering.github.io/opensmile/.
History
The toolkit was first developed at the Institute for Human-Machine
Communication at the Technische Universität München in Munich, Germany.
It was started within the SEMAINE EU-FP7 research project.
The toolkit is now owned and maintained
by audEERING GmbH, who provide intelligent
audio analysis solutions, automatic speech emotion recognition,
and paralinguistic speech analysis software packages as well as
consulting and development services on these topics.
Contributing and Support
We welcome contributions! For feedback and technical support,
please use the issue tracker.
Licensing
openSMILE follows a dual-licensing model. Since the main goal of the project
is a widespread use of the software to facilitate research in the field of
machine learning from audio-visual signals, the source code and binaries are
freely available for private, research, and educational use under an open-source license
(see LICENSE).
It is not allowed to use the open-source version of openSMILE for any sort of commercial product.
Fundamental research in companies, for example, is permitted, but if a product is the result of
the research, we require you to buy a commercial development license.
Contact us at info@audeering.com (or visit us at https://www.audeering.com) for more information.
Original authors: Florian Eyben, Felix Weninger, Martin Wöllmer, Björn Schuller

Copyright © 2008-2013, Institute for Human-Machine Communication, Technische Universität München, Germany

Copyright © 2013-2015, audEERING UG (haftungsbeschränkt)

Copyright © 2016-2020, audEERING GmbH
Citing
Please cite openSMILE in your publications by citing the following paper:
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
About
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering.github.io/opensmile/
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages