From d171bfa8483f4130272df9f469fb821119b9f893 Mon Sep 17 00:00:00 2001 From: hagenw Date: Wed, 3 Jan 2024 13:55:02 +0000 Subject: [PATCH] deploy: 5c9d40cd13f5eabd863a1cdb416cf8a2a1d3bc60 --- .doctrees/datasets.doctree | Bin 7515 -> 17276 bytes .doctrees/datasets/air.doctree | Bin 0 -> 21817 bytes .../datasets/cough-speech-sneeze.doctree | Bin 0 -> 18525 bytes .doctrees/datasets/crema-d.doctree | Bin 0 -> 44060 bytes .doctrees/datasets/emodb.doctree | Bin 23397 -> 23545 bytes .doctrees/datasets/micirp.doctree | Bin 0 -> 15788 bytes .doctrees/datasets/musan.doctree | Bin 0 -> 25811 bytes .doctrees/datasets/vadtoolkit.doctree | Bin 0 -> 15301 bytes .doctrees/environment.pickle | Bin 19133 -> 33653 bytes _images/air.png | Bin 0 -> 377 bytes _images/cough-speech-sneeze.png | Bin 0 -> 3518 bytes _images/crema-d.png | Bin 0 -> 2568 bytes _images/emodb.png | Bin 0 -> 5482 bytes _images/micirp.png | Bin 0 -> 514 bytes _images/musan.png | Bin 0 -> 4950 bytes _images/vadtoolkit.png | Bin 0 -> 5803 bytes _sources/datasets.rst.txt | 6 + _sources/datasets/air.rst.txt | 64 +++ _sources/datasets/cough-speech-sneeze.rst.txt | 59 ++ _sources/datasets/crema-d.rst.txt | 96 ++++ _sources/datasets/emodb.rst.txt | 6 +- _sources/datasets/micirp.rst.txt | 58 ++ _sources/datasets/musan.rst.txt | 77 +++ _sources/datasets/vadtoolkit.rst.txt | 58 ++ _static/pygments.css | 1 + changelog.html | 4 +- contributing.html | 8 +- datasets.html | 56 +- datasets/air.html | 361 ++++++++++++ datasets/cough-speech-sneeze.html | 334 ++++++++++++ datasets/crema-d.html | 512 ++++++++++++++++++ datasets/emodb.html | 23 +- datasets/micirp.html | 330 +++++++++++ datasets/musan.html | 406 ++++++++++++++ datasets/vadtoolkit.html | 333 ++++++++++++ genindex.html | 4 +- index.html | 4 +- objects.inv | 7 +- search.html | 4 +- searchindex.js | 2 +- 40 files changed, 2779 insertions(+), 34 deletions(-) create mode 100644 .doctrees/datasets/air.doctree create mode 100644 .doctrees/datasets/cough-speech-sneeze.doctree 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100644 index 0000000..277d549 --- /dev/null +++ b/_sources/datasets/air.rst.txt @@ -0,0 +1,64 @@ +.. _air: + +air +--- + +Created by Marco Jeub, Magnus Schäfer, Hauke Krüger, Christoph Matthias Nelke, Christophe Beaugeant, Peter Vary + + +============= ====================== +version `1.4.2 `__ +license `MIT `__ +source https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases/aachen-impulse-response-database/ +usage commercial +languages +format wav +channel 2 +sampling rate 48000 +bit depth 16 +duration 0 days 00:04:43.719958333 +files 107 +repository `data-public `__ +published 2023-12-21 by audeering-unittest +============= ====================== + + +Description +^^^^^^^^^^^ + +The Aachen Impulse Response (AIR) database is a set of impulse responses that were measured in a wide variety of rooms. The initial aim of the AIR database was to allow for realistic studies of signal processing algorithms in reverberant environments with a special focus on hearing aids applications. The first version was published in 2009 and offers binaural room impulse responses (BRIR) measured with a dummy head in different locations with different acoustical properties, such as reverberation time and room volume. Besides the evaluation of dereverberation algorithms and perceptual investigations of reverberant speech, this part of the database allows for the investigation of head shadowing influence since all recordings where made with and without the dummy head. In a first update, the database was extended to BRIRs with various azimuth angles between head and desired source. This further allows to investigate (binaural) direction-of-arrival (DOA) algorithms as well as the influence of signal processing algorithms on the binaural cues. Since dereverberation can also be applied to telephone speech, the latest extension includes (dual-channel) impulse responses between the artificial mouth of a dummy head and a mock-up phone. The measurements were carried out in compliance with the ITU standards for both the hand-held and the hands-free position. Additional microphone configurations were added in the latest extension. For the third big extension, the IKS has carried out measurements of binaural room impulse responses in the Aula Carolina Aachen. The former church with a ground area of 570m² and a high ceiling shows very strong reverberation effects. The database will successively be extended to further application scenarios. + +Example +^^^^^^^ + +:file:`data/air_binaural_stairway_1_1_0.wav` + +.. image:: ../air.png + +.. raw:: html + +

+ +Tables +^^^^^^ + +.. csv-table:: + :header: ID,Type,Columns + :widths: 20, 10, 70 + + "brir", "filewise", "room, azimuth" + "phone", "filewise", "room, mode" + "rir", "filewise", "room, distance, reverberation-time" + + +Schemes +^^^^^^^ + +.. csv-table:: + :header: ID,Dtype,Labels,Mappings + + "azimuth", "float", "" + "distance", "float", "" + "mode", "str", "hand-held, hands-free" + "reverberation-time", "float", "" + "room", "str", "aula_carolina, bathroom, booth, corridor, kitchen, lecture, meeting, office, stairway", "floor cover, furniture, room height, room length, room width, wall surface" diff --git a/_sources/datasets/cough-speech-sneeze.rst.txt b/_sources/datasets/cough-speech-sneeze.rst.txt new file mode 100644 index 0000000..5c45a98 --- /dev/null +++ b/_sources/datasets/cough-speech-sneeze.rst.txt @@ -0,0 +1,59 @@ +.. _cough-speech-sneeze: + +cough-speech-sneeze +------------------- + +Created by S Amiriparian, S Pugachevskiy, N Cummins, D Hantke, J Pohjalainen, G Keren, Schuller, BW + + +============= ====================== +version `2.0.1 `__ +license `CC-BY-4.0 `__ +source Dataset based on the publication of Shahin Amiriparian: "Amiriparian, S., Pugachevskiy, S., Cummins, N., Hantke, S., Pohjalainen, J., Keren, G., Schuller, B., 2017. CAST a database: Rapid targeted large-scale big data acquisition via small-world modelling of social media platforms, in: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, pp. 340–345. https://doi.org/10.1109/ACII.2017.8273622" +usage commercial +languages +format wav +channel 1 +sampling rate 16000, 44100 +bit depth 16 +duration 0 days 03:02:29.436148526 +files 4310 +repository `data-public `__ +published 2024-01-02 by audeering +============= ====================== + + +Description +^^^^^^^^^^^ + +Cough-speech-sneeze: a data set of human sounds This dataset was collected by Dr. Shahin Amiriparian. It contains samples of human speech, coughing, and sneezing collected from YouTube, as well as silence clips. The original publication of this (possibly then extended) dataset is the following: Amiriparian, S., Pugachevskiy, S., Cummins, N., Hantke, S., Pohjalainen, J., Keren, G., Schuller, B., 2017. CAST a database: Rapid targeted large-scale big data acquisition via small-world modelling of social media platforms, in: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, pp. 340–345. https://doi.org/10.1109/ACII.2017.8273622 + +Example +^^^^^^^ + +:file:`coughing/6hw6_4eb_hq_18.41-19.81.wav` + +.. image:: ../cough-speech-sneeze.png + +.. raw:: html + +

+ +Tables +^^^^^^ + +.. csv-table:: + :header: ID,Type,Columns + :widths: 20, 10, 70 + + "files", "filewise", "category, duration" + + +Schemes +^^^^^^^ + +.. csv-table:: + :header: ID,Dtype,Labels + + "category", "str", "coughing, silence, sneezing, speech" + "duration", "time", "" diff --git a/_sources/datasets/crema-d.rst.txt b/_sources/datasets/crema-d.rst.txt new file mode 100644 index 0000000..35e03ae --- /dev/null +++ b/_sources/datasets/crema-d.rst.txt @@ -0,0 +1,96 @@ +.. _crema-d: + +crema-d +------- + +Created by Houwei Cao, David G. Cooper, Michael K. Keutmann, Ruben C. Gur, Ani Nenkova, Ragini Verma, Samantha L Moore, Adam Savitt + + +============= ====================== +version `1.2.0 `__ +license `Open Data Commons Open Database License (ODbL) v1.0 `__ +source https://github.com/CheyneyComputerScience/CREMA-D +usage commercial +languages English +format wav +channel 1 +sampling rate 16000 +bit depth 16 +duration 0 days 05:15:21.404187500 +files 7441 +repository `data-public `__ +published 2024-01-02 by audeering +============= ====================== + + +Description +^^^^^^^^^^^ + +CREMA-D: Crowd-sourced Emotional Mutimodal Actors Dataset CREMA-D is a data set of 7,442 original clips from 91 actors. These clips were from 48 male and 43 female actors between the ages of 20 and 74 coming from a variety of races and ethnicities (African America, Asian, Caucasian, Hispanic, and Unspecified). When using the database commercially, the database must be referenced together with its license. + +Example +^^^^^^^ + +:file:`1001/1001_TAI_HAP_XX.wav` + +.. image:: ../crema-d.png + +.. raw:: html + +

+ +Tables +^^^^^^ + +.. csv-table:: + :header: ID,Type,Columns + :widths: 20, 10, 70 + + "emotion.categories.desired.dev", "filewise", "emotion, emotion.intensity" + "emotion.categories.desired.test", "filewise", "emotion, emotion.intensity" + "emotion.categories.desired.train", "filewise", "emotion, emotion.intensity" + "emotion.categories.dev", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level" + "emotion.categories.dev.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.dev.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.face.dev", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level" + "emotion.categories.face.dev.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.face.dev.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.face.test", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level" + "emotion.categories.face.test.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.face.test.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.face.train", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level" + "emotion.categories.face.train.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.face.train.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.multimodal.dev", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level" + "emotion.categories.multimodal.dev.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.multimodal.dev.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.multimodal.test", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level" + "emotion.categories.multimodal.test.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.multimodal.test.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.multimodal.train", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level" + "emotion.categories.multimodal.train.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.multimodal.train.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.test", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level" + "emotion.categories.test.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.test.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "emotion.categories.train", "filewise", "emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level" + "emotion.categories.train.gold_standard", "filewise", "emotion, emotion.level, emotion.agreement" + "emotion.categories.train.votes", "filewise", "anger, disgust, fear, happiness, neutral, sadness" + "files", "filewise", "speaker, corrupted" + "sentence", "filewise", "sentence" + + +Schemes +^^^^^^^ + +.. csv-table:: + :header: ID,Dtype,Min,Max,Labels,Mappings + + "corrupted", "bool", "", "", "" + "emotion", "str", "", "", "anger, disgust, fear, happiness, neutral, no_agreement, sadness" + "emotion.agreement", "float", "", "1", "" + "emotion.intensity", "str", "", "", "high, low, mid, unspecified" + "emotion.level", "float", "", "100", "" + "sentence", "str", "", "", "DFA, IEO, IOM, ITH, ITS, IWL, IWW, MTI, TAI, TIE, TSI, WSI", "✓" + "speaker", "int", "", "", "1001, 1002, 1003, 1004, 1005, 1006, 1007, [...], 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091", "age, ethnicity, race, sex" + "votes", "int", "", "", "" diff --git a/_sources/datasets/emodb.rst.txt b/_sources/datasets/emodb.rst.txt index cc45642..0a2ca8b 100644 --- a/_sources/datasets/emodb.rst.txt +++ b/_sources/datasets/emodb.rst.txt @@ -7,7 +7,7 @@ Created by Felix Burkhardt, Astrid Paeschke, Miriam Rolfes, Walter Sendlmeier, B ============= ====================== -version `1.3.0 `__ +version `1.4.1 `__ license `CC0-1.0 `__ source http://emodb.bilderbar.info/download/download.zip usage unrestricted @@ -19,7 +19,7 @@ bit depth 16 duration 0 days 00:24:47.092187500 files 535 repository `data-public `__ -published 2022-08-05 by audeering-unittest +published 2023-04-05 by audeering-unittest ============= ====================== @@ -33,6 +33,8 @@ Example :file:`wav/13b09La.wav` +.. image:: ../emodb.png + .. raw:: html

diff --git a/_sources/datasets/micirp.rst.txt b/_sources/datasets/micirp.rst.txt new file mode 100644 index 0000000..8aec053 --- /dev/null +++ b/_sources/datasets/micirp.rst.txt @@ -0,0 +1,58 @@ +.. _micirp: + +micirp +------ + +Created by Stewart Tavener (Xaudia.com) + + +============= ====================== +version `1.0.0 `__ +license `CC-BY-SA-4.0 `__ +source http://micirp.blogspot.com/ +usage commercial +languages +format wav +channel 1 +sampling rate 44100, 48000 +bit depth 24 +duration 0 days 00:00:27.341591837 +files 66 +repository `data-public `__ +published 2023-12-21 by audeering +============= ====================== + + +Description +^^^^^^^^^^^ + +The Microphone Impulse Response Project (MicIRP) contains impulse response data for vintage microphones. The impulse response files were created using the analysis software Fuzzmeasure. The microphones were tested using a swept-sine method in a small booth, treated with much acoustic foam, placed about 20 to 30 cm from the source. Although the recording system and booth are calibrated regularly with a Beyerdynamic measurement microphone, there are problems comparing, for example, a figure-8 ribbon with an omnidirectional standard, as they will see different amounts of reflections from the side. So, it should be noted that the impulse response files describe the microphones measured in the booth, rather than in free space. + +Example +^^^^^^^ + +:file:`dirs/IR_AKGD12.wav` + +.. image:: ../micirp.png + +.. raw:: html + +

+ +Tables +^^^^^^ + +.. csv-table:: + :header: ID,Type,Columns + :widths: 20, 10, 70 + + "files", "filewise", "manufacturer" + + +Schemes +^^^^^^^ + +.. csv-table:: + :header: ID,Dtype,Labels + + "manufacturer", "str", "AKG, Altec, American, Amperite, Astatic, B&O, BBC, [...], Oktava, RCA, Reslo, STC, Shure, Sony, Telefunken, Toshiba" diff --git a/_sources/datasets/musan.rst.txt b/_sources/datasets/musan.rst.txt new file mode 100644 index 0000000..8e92982 --- /dev/null +++ b/_sources/datasets/musan.rst.txt @@ -0,0 +1,77 @@ +.. _musan: + +musan +----- + +Created by David Snyder, Guoguo Chen, Daniel Povey + + +============= ====================== +version `1.0.0 `__ +license `CC-BY-4.0 `__ +source http://www.openslr.org/17/ +usage commercial +languages ara, zho, dan, nld, eng, fra, deu, heb, hun, ita, jpn, lat, pol, por, rus, spa, tgl +format wav +channel 1 +sampling rate 16000 +bit depth 16 +duration 4 days 13:17:22.582937499 +files 2016 +repository `data-public `__ +published 2023-12-20 by audeering-unittest +============= ====================== + + +Description +^^^^^^^^^^^ + +The goal of this corpus is to provide data for music/speech discrimination, speech/nonspeech detection, and voice activity detection. The corpus is divided into music, speech, and noise portions. In total there are approximately 109 hours of audio. Reference: https://arxiv.org/abs/1510.08484 + +Example +^^^^^^^ + +:file:`noise/free-sound/noise-free-sound-0324.wav` + +.. image:: ../musan.png + +.. raw:: html + +

+ +Tables +^^^^^^ + +.. csv-table:: + :header: ID,Type,Columns + :widths: 20, 10, 70 + + "files", "filewise", "duration" + "music", "filewise", "genre, vocals, artist, composer" + "music.fma", "filewise", "genre, vocals, artist, composer" + "music.fma-western-art", "filewise", "genre, vocals, artist, composer" + "music.hd-classical", "filewise", "genre, vocals, artist, composer" + "music.jamendo", "filewise", "genre, vocals, artist, composer" + "music.rfm", "filewise", "genre, vocals, artist, composer" + "noise", "filewise", "background_noise" + "noise.free-sound", "filewise", "background_noise" + "noise.sound-bible", "filewise", "background_noise" + "speech", "filewise", "gender, language" + "speech.librivox", "filewise", "gender, language" + "speech.us-gov", "filewise", "gender, language" + + +Schemes +^^^^^^^ + +.. csv-table:: + :header: ID,Dtype,Labels + + "artist", "str", "" + "background_noise", "bool", "" + "composer", "str", "" + "duration", "time", "" + "gender", "str", "female, male" + "genre", "str", "" + "language", "str", "ara, dan, deu, eng, fra, heb, hun, [...], lat, nld, pol, por, rus, spa, tgl, zho" + "vocals", "bool", "" diff --git a/_sources/datasets/vadtoolkit.rst.txt b/_sources/datasets/vadtoolkit.rst.txt new file mode 100644 index 0000000..332e3eb --- /dev/null +++ b/_sources/datasets/vadtoolkit.rst.txt @@ -0,0 +1,58 @@ +.. _vadtoolkit: + +vadtoolkit +---------- + +Created by Kim Jaeseok + + +============= ====================== +version `1.1.0 `__ +license `GPLv3 `__ +source https://github.com/jtkim-kaist/VAD +usage commercial +languages kor +format wav +channel 1 +sampling rate 16000 +bit depth 32, 16 +duration 0 days 02:00:09.703062500 +files 4 +repository `data-public `__ +published 2024-01-02 by audeering +============= ====================== + + +Description +^^^^^^^^^^^ + +VAD Toolkit: A Database for Voice Activity Detection At each environment, conversational speech by two Korean male speakers was recorded. The ground truth labels are manually annotated. Because the recording was carried out in the real world, unexpected noises are included to the dataset such as the crying of baby, the chirping of insects, mouse click sound, and etc.. + +Example +^^^^^^^ + +:file:`park.wav` + +.. image:: ../vadtoolkit.png + +.. raw:: html + +

+ +Tables +^^^^^^ + +.. csv-table:: + :header: ID,Type,Columns + :widths: 20, 10, 70 + + "segments", "segmented", "noise" + + +Schemes +^^^^^^^ + +.. csv-table:: + :header: ID,Dtype,Labels,Mappings + + "noise", "int", "0, 1, 2, 3", "✓" diff --git a/_static/pygments.css b/_static/pygments.css index 7a18115..8054382 100644 --- a/_static/pygments.css +++ b/_static/pygments.css @@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: .highlight .cs { color: #60a0b0; background-color: #fff0f0 } /* Comment.Special */ .highlight .gd { color: #A00000 } /* Generic.Deleted */ .highlight .ge { font-style: italic } /* Generic.Emph */ +.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */ .highlight .gr { color: #FF0000 } /* Generic.Error */ .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */ .highlight .gi { color: #00A000 } /* Generic.Inserted */ diff --git a/changelog.html b/changelog.html index c5ae0a7..fcf51fa 100644 --- a/changelog.html +++ b/changelog.html @@ -63,7 +63,7 @@
- v1.0.1 + v5c9d40c
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Version 1.0.0 (2023-03-20)Sphinx on 2023/03/20 using the audEERING theme + Built with Sphinx on 2024/01/03 using the audEERING theme

diff --git a/contributing.html b/contributing.html index 7bd64d9..a02f849 100644 --- a/contributing.html +++ b/contributing.html @@ -31,7 +31,7 @@ - + @@ -64,7 +64,7 @@
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Creating a New Release - + @@ -224,7 +224,7 @@

Creating a New ReleaseSphinx on 2023/03/20 using the audEERING theme + Built with Sphinx on 2024/01/03 using the audEERING theme

diff --git a/datasets.html b/datasets.html index bef5cc5..c571a68 100644 --- a/datasets.html +++ b/datasets.html @@ -30,7 +30,7 @@ - + @@ -64,7 +64,7 @@
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Overview

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Datasets

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Datasets available with audb as of Mar 20, 2023. +

Datasets available with audb as of Jan 03, 2024. For each dataset, the latest version is shown.

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emodb

air

The Aachen Impulse Response (AIR) database is a set of impulse responses that were measured in a wide variety of rooms. The initial aim of the AIR …

MIT

1.4.2

azimuth, distance, mode, reverberation-time, room

cough-speech-sneeze

Cough-speech-sneeze: a data set of human sounds This dataset was collected by Dr. Shahin Amiriparian. It contains samples of human speech, coughing…

CC-BY-4.0

2.0.1

category

crema-d

CREMA-D: Crowd-sourced Emotional Mutimodal Actors Dataset CREMA-D is a data set of 7,442 original clips from 91 actors. These clips were from 48 m…

Open Data Commons Open Database License (ODbL) v1.0

1.2.0

emotion: [anger, disgust, fear, happiness, neutral, no_agreement, sadness], speaker: [age, sex, race, ethnicity], corrupted, emotion.agreement, emotion.intensity, emotion.level, sentence, votes

emodb

Berlin Database of Emotional Speech. A German database of emotional utterances spoken by actors recorded as a part of the DFG funded research proje…

CC0-1.0

1.3.0

1.4.1

emotion: [anger, boredom, disgust, fear, happiness, sadness, neutral], speaker: [age, gender, language], age, confidence, gender, language, transcription

micirp

The Microphone Impulse Response Project (MicIRP) contains impulse response data for vintage microphones. The impulse response files were created us…

CC-BY-SA-4.0

1.0.0

manufacturer

musan

The goal of this corpus is to provide data for music/speech discrimination, speech/nonspeech detection, and voice activity detection. The corpus is…

CC-BY-4.0

1.0.0

artist, background_noise, composer, gender, genre, language, vocals

vadtoolkit

VAD Toolkit: A Database for Voice Activity Detection At each environment, conversational speech by two Korean male speakers was recorded. The groun…

GPLv3

1.1.0

noise
@@ -176,7 +218,7 @@ diff --git a/datasets/air.html b/datasets/air.html new file mode 100644 index 0000000..b858528 --- /dev/null +++ b/datasets/air.html @@ -0,0 +1,361 @@ + + + + + + + + + + + air — datasets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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air

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Created by Marco Jeub, Magnus Schäfer, Hauke Krüger, Christoph Matthias Nelke, Christophe Beaugeant, Peter Vary

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

version

1.4.2

license

MIT

source

https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases/aachen-impulse-response-database/

usage

commercial

languages

format

wav

channel

2

sampling rate

48000

bit depth

16

duration

0 days 00:04:43.719958333

files

107

repository

data-public

published

2023-12-21 by audeering-unittest

+
+

Description

+

The Aachen Impulse Response (AIR) database is a set of impulse responses that were measured in a wide variety of rooms. The initial aim of the AIR database was to allow for realistic studies of signal processing algorithms in reverberant environments with a special focus on hearing aids applications. The first version was published in 2009 and offers binaural room impulse responses (BRIR) measured with a dummy head in different locations with different acoustical properties, such as reverberation time and room volume. Besides the evaluation of dereverberation algorithms and perceptual investigations of reverberant speech, this part of the database allows for the investigation of head shadowing influence since all recordings where made with and without the dummy head. In a first update, the database was extended to BRIRs with various azimuth angles between head and desired source. This further allows to investigate (binaural) direction-of-arrival (DOA) algorithms as well as the influence of signal processing algorithms on the binaural cues. Since dereverberation can also be applied to telephone speech, the latest extension includes (dual-channel) impulse responses between the artificial mouth of a dummy head and a mock-up phone. The measurements were carried out in compliance with the ITU standards for both the hand-held and the hands-free position. Additional microphone configurations were added in the latest extension. For the third big extension, the IKS has carried out measurements of binaural room impulse responses in the Aula Carolina Aachen. The former church with a ground area of 570m² and a high ceiling shows very strong reverberation effects. The database will successively be extended to further application scenarios.

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Example

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data/air_binaural_stairway_1_1_0.wav

+../_images/air.png +

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Tables

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ID

Type

Columns

brir

filewise

room, azimuth

phone

filewise

room, mode

rir

filewise

room, distance, reverberation-time

+
+
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Schemes

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

ID

Dtype

Labels

Mappings

azimuth

float

distance

float

mode

str

hand-held, hands-free

reverberation-time

float

room

str

aula_carolina, bathroom, booth, corridor, kitchen, lecture, meeting, office, stairway

floor cover, furniture, room height, room length, room width, wall surface

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cough-speech-sneeze

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Created by S Amiriparian, S Pugachevskiy, N Cummins, D Hantke, J Pohjalainen, G Keren, Schuller, BW

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version

2.0.1

license

CC-BY-4.0

source

Dataset based on the publication of Shahin Amiriparian: “Amiriparian, S., Pugachevskiy, S., Cummins, N., Hantke, S., Pohjalainen, J., Keren, G., Schuller, B., 2017. CAST a database: Rapid targeted large-scale big data acquisition via small-world modelling of social media platforms, in: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, pp. 340–345. https://doi.org/10.1109/ACII.2017.8273622”

usage

commercial

languages

format

wav

channel

1

sampling rate

16000, 44100

bit depth

16

duration

0 days 03:02:29.436148526

files

4310

repository

data-public

published

2024-01-02 by audeering

+
+

Description

+

Cough-speech-sneeze: a data set of human sounds This dataset was collected by Dr. Shahin Amiriparian. It contains samples of human speech, coughing, and sneezing collected from YouTube, as well as silence clips. The original publication of this (possibly then extended) dataset is the following: Amiriparian, S., Pugachevskiy, S., Cummins, N., Hantke, S., Pohjalainen, J., Keren, G., Schuller, B., 2017. CAST a database: Rapid targeted large-scale big data acquisition via small-world modelling of social media platforms, in: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, pp. 340–345. https://doi.org/10.1109/ACII.2017.8273622

+
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Example

+

coughing/6hw6_4eb_hq_18.41-19.81.wav

+../_images/cough-speech-sneeze.png +

+
+

Tables

+ +++++ + + + + + + + + + + + + +

ID

Type

Columns

files

filewise

category, duration

+
+
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Schemes

+ +++++ + + + + + + + + + + + + + + + + +

ID

Dtype

Labels

category

str

coughing, silence, sneezing, speech

duration

time

+
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/datasets/crema-d.html b/datasets/crema-d.html new file mode 100644 index 0000000..ed8aa3d --- /dev/null +++ b/datasets/crema-d.html @@ -0,0 +1,512 @@ + + + + + + + + + + + crema-d — datasets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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crema-d

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Created by Houwei Cao, David G. Cooper, Michael K. Keutmann, Ruben C. Gur, Ani Nenkova, Ragini Verma, Samantha L Moore, Adam Savitt

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

version

1.2.0

license

Open Data Commons Open Database License (ODbL) v1.0

source

https://github.com/CheyneyComputerScience/CREMA-D

usage

commercial

languages

English

format

wav

channel

1

sampling rate

16000

bit depth

16

duration

0 days 05:15:21.404187500

files

7441

repository

data-public

published

2024-01-02 by audeering

+
+

Description

+

CREMA-D: Crowd-sourced Emotional Mutimodal Actors Dataset CREMA-D is a data set of 7,442 original clips from 91 actors. These clips were from 48 male and 43 female actors between the ages of 20 and 74 coming from a variety of races and ethnicities (African America, Asian, Caucasian, Hispanic, and Unspecified). When using the database commercially, the database must be referenced together with its license.

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Example

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1001/1001_TAI_HAP_XX.wav

+../_images/crema-d.png +

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Tables

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ID

Type

Columns

emotion.categories.desired.dev

filewise

emotion, emotion.intensity

emotion.categories.desired.test

filewise

emotion, emotion.intensity

emotion.categories.desired.train

filewise

emotion, emotion.intensity

emotion.categories.dev

filewise

emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level

emotion.categories.dev.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.dev.votes

filewise

anger, disgust, fear, happiness, neutral, sadness

emotion.categories.face.dev

filewise

emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level

emotion.categories.face.dev.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.face.dev.votes

filewise

anger, disgust, fear, happiness, neutral, sadness

emotion.categories.face.test

filewise

emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level

emotion.categories.face.test.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.face.test.votes

filewise

anger, disgust, fear, happiness, neutral, sadness

emotion.categories.face.train

filewise

emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level

emotion.categories.face.train.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.face.train.votes

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anger, disgust, fear, happiness, neutral, sadness

emotion.categories.multimodal.dev

filewise

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emotion.categories.multimodal.dev.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.multimodal.dev.votes

filewise

anger, disgust, fear, happiness, neutral, sadness

emotion.categories.multimodal.test

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emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level

emotion.categories.multimodal.test.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.multimodal.test.votes

filewise

anger, disgust, fear, happiness, neutral, sadness

emotion.categories.multimodal.train

filewise

emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level

emotion.categories.multimodal.train.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.multimodal.train.votes

filewise

anger, disgust, fear, happiness, neutral, sadness

emotion.categories.test

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emotion.categories.test.votes

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anger, disgust, fear, happiness, neutral, sadness

emotion.categories.train

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emotion.0, emotion.0.level, emotion.1, emotion.1.level, emotion.2, emotion.2.level, emotion.3, emotion.3.level, emotion.4, emotion.4.level

emotion.categories.train.gold_standard

filewise

emotion, emotion.level, emotion.agreement

emotion.categories.train.votes

filewise

anger, disgust, fear, happiness, neutral, sadness

files

filewise

speaker, corrupted

sentence

filewise

sentence

+
+
+

Schemes

+ ++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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100

sentence

str

DFA, IEO, IOM, ITH, ITS, IWL, IWW, MTI, TAI, TIE, TSI, WSI

✓

speaker

int

1001, 1002, 1003, 1004, 1005, 1006, 1007, […], 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091

age, ethnicity, race, sex

votes

int

+
+
+ + +
+ +
+ +
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- v1.0.1 + v5c9d40c
@@ -91,6 +91,9 @@

Overview

@@ -152,7 +158,7 @@

version

-

1.3.0

+

1.4.1

license

CC0-1.0

@@ -188,7 +194,7 @@

data-public

published

-

2022-08-05 by audeering-unittest

+

2023-04-05 by audeering-unittest

@@ -199,6 +205,7 @@

Description

Example

wav/13b09La.wav

+../_images/emodb.png

Tables

@@ -328,10 +335,10 @@

Schemes - + - +

@@ -343,7 +350,7 @@

SchemesSphinx on 2023/03/20 using the audEERING theme + Built with Sphinx on 2024/01/03 using the audEERING theme

diff --git a/datasets/micirp.html b/datasets/micirp.html new file mode 100644 index 0000000..0993ff6 --- /dev/null +++ b/datasets/micirp.html @@ -0,0 +1,330 @@ + + + + + + + + + + + micirp — datasets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ +
+ +
+
+
+
+ +
+

micirp

+

Created by Stewart Tavener (Xaudia.com)

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

version

1.0.0

license

CC-BY-SA-4.0

source

http://micirp.blogspot.com/

usage

commercial

languages

format

wav

channel

1

sampling rate

44100, 48000

bit depth

24

duration

0 days 00:00:27.341591837

files

66

repository

data-public

published

2023-12-21 by audeering

+
+

Description

+

The Microphone Impulse Response Project (MicIRP) contains impulse response data for vintage microphones. The impulse response files were created using the analysis software Fuzzmeasure. The microphones were tested using a swept-sine method in a small booth, treated with much acoustic foam, placed about 20 to 30 cm from the source. Although the recording system and booth are calibrated regularly with a Beyerdynamic measurement microphone, there are problems comparing, for example, a figure-8 ribbon with an omnidirectional standard, as they will see different amounts of reflections from the side. So, it should be noted that the impulse response files describe the microphones measured in the booth, rather than in free space.

+
+
+

Example

+

dirs/IR_AKGD12.wav

+../_images/micirp.png +

+
+

Tables

+ +++++ + + + + + + + + + + + + +

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Type

Columns

files

filewise

manufacturer

+
+
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Schemes

+ +++++ + + + + + + + + + + + + +

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Dtype

Labels

manufacturer

str

AKG, Altec, American, Amperite, Astatic, B&O, BBC, […], Oktava, RCA, Reslo, STC, Shure, Sony, Telefunken, Toshiba

+
+
+ + +
+ +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/datasets/musan.html b/datasets/musan.html new file mode 100644 index 0000000..6fd332c --- /dev/null +++ b/datasets/musan.html @@ -0,0 +1,406 @@ + + + + + + + + + + + musan — datasets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ +
+ +
+
+
+
+ +
+

musan

+

Created by David Snyder, Guoguo Chen, Daniel Povey

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

version

1.0.0

license

CC-BY-4.0

source

http://www.openslr.org/17/

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commercial

languages

ara, zho, dan, nld, eng, fra, deu, heb, hun, ita, jpn, lat, pol, por, rus, spa, tgl

format

wav

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1

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16000

bit depth

16

duration

4 days 13:17:22.582937499

files

2016

repository

data-public

published

2023-12-20 by audeering-unittest

+
+

Description

+

The goal of this corpus is to provide data for music/speech discrimination, speech/nonspeech detection, and voice activity detection. The corpus is divided into music, speech, and noise portions. In total there are approximately 109 hours of audio. Reference: https://arxiv.org/abs/1510.08484

+
+
+

Example

+

noise/free-sound/noise-free-sound-0324.wav

+../_images/musan.png +

+
+

Tables

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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genre, vocals, artist, composer

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genre, vocals, artist, composer

music.hd-classical

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genre, vocals, artist, composer

music.jamendo

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genre, vocals, artist, composer

music.rfm

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genre, vocals, artist, composer

noise

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background_noise

noise.free-sound

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noise.sound-bible

filewise

background_noise

speech

filewise

gender, language

speech.librivox

filewise

gender, language

speech.us-gov

filewise

gender, language

+
+
+

Schemes

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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Dtype

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artist

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background_noise

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female, male

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language

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ara, dan, deu, eng, fra, heb, hun, […], lat, nld, pol, por, rus, spa, tgl, zho

vocals

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+
+
+ + +
+ +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/datasets/vadtoolkit.html b/datasets/vadtoolkit.html new file mode 100644 index 0000000..43c6313 --- /dev/null +++ b/datasets/vadtoolkit.html @@ -0,0 +1,333 @@ + + + + + + + + + + + vadtoolkit — datasets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ +
+ +
+
+
+
+ +
+

vadtoolkit

+

Created by Kim Jaeseok

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

version

1.1.0

license

GPLv3

source

https://github.com/jtkim-kaist/VAD

usage

commercial

languages

kor

format

wav

channel

1

sampling rate

16000

bit depth

32, 16

duration

0 days 02:00:09.703062500

files

4

repository

data-public

published

2024-01-02 by audeering

+
+

Description

+

VAD Toolkit: A Database for Voice Activity Detection At each environment, conversational speech by two Korean male speakers was recorded. The ground truth labels are manually annotated. Because the recording was carried out in the real world, unexpected noises are included to the dataset such as the crying of baby, the chirping of insects, mouse click sound, and etc..

+
+
+

Example

+

park.wav

+../_images/vadtoolkit.png +

+
+

Tables

+ +++++ + + + + + + + + + + + + +

ID

Type

Columns

segments

segmented

noise

+
+
+

Schemes

+ ++++++ + + + + + + + + + + + + + + +

ID

Dtype

Labels

Mappings

noise

int

0, 1, 2, 3

✓

+
+
+ + +
+ +
+ +
+
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+ +
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- v1.0.1 + v5c9d40c
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Index

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diff --git a/index.html b/index.html index d29bce9..972e36d 100644 --- a/index.html +++ b/index.html @@ -63,7 +63,7 @@
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datasetsSphinx on 2023/03/20 using the audEERING theme + Built with Sphinx on 2024/01/03 using the audEERING theme

diff --git a/objects.inv b/objects.inv index 4ebe762..eee73d2 100644 --- a/objects.inv +++ b/objects.inv @@ -1,7 +1,6 @@ # Sphinx inventory version 2 # Project: datasets -# Version: v1.0.1 +# Version: v5c9d40c # The remainder of this file is compressed using zlib. -xڅ -0DVU/_&KRH1[ouЂݙ73Jo#"J4'x48q/w#z -hI2"25ƯQ';t+x_G /y5U.}OX_d{УT\bD6:Qq~+   \ No newline at end of file +xڅn <I{M{m/=4٤O0QRt *'gdREm^`9IA=<҆XHdt$hoe;y@L7~dqb5S4pȷxJ _VYs_WLu*}Z)nh - v1.0.1 + v5c9d40c @@ -157,7 +157,7 @@ - Built with Sphinx on 2023/03/20 using the audEERING theme + Built with Sphinx on 2024/01/03 using the audEERING theme

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