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Automatically generated CSV and README from JSON update
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -18,7 +18,7 @@ The table can be browsed, sorted and searched under https://superkogito.github.i
| <sub>[MESD](https://data.mendeley.com/datasets/cy34mh68j9/5)</sub> | <sub>2022</sub> | <sub>864 audio files of single-word emotional utterances with Mexican cultural shaping.</sub> | <sub>6 emotions provides single-word utterances for anger, disgust, fear, happiness, neutral, and sadness.</sub> | <sub>Audio</sub> | <sub>0.097 GB</sub> | <sub>Spanish (Mexican)</sub> | <sub>[The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning](https://pubmed.ncbi.nlm.nih.gov/34891601/)</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
| <sub>[SyntAct](https://zenodo.org/record/6573016#.ZAjy_9LMJpj)</sub> | <sub>2022</sub> | <sub>Synthesized database with 997 utterances of three basic emotions and neutral expression based on rule-based manipulation for a diphone synthesizer which we release to the public </sub> | <sub>6 emotions: angry, bored, happy, neutral, sad and scared</sub> | <sub>Audio</sub> | <sub>0.941 GB</sub> | <sub>German</sub> | <sub>[SyntAct: A Synthesized Database of Basic Emotions](http://felix.syntheticspeech.de/publications/synthetic_database.pdf)</sub> | <sub>Open</sub> | <sub>[CC BY-SA 4.0](https://creativecommons.org/licenses/by/4.0)</sub> |
| <sub>[BEAT](https://drive.google.com/drive/folders/1EKuWH8q178QOtFUYaNohdkZbBHQYAmhL)</sub> | <sub>2022</sub> | <sub>76-Hour and 30-Speaker of 4 different languages: English (60h), Chinese (12h), Spanish (2h) and Japanese (2h).</sub> | <sub>8 emotions: happiness, anger, disgust, sadness, contempt, surprise, fear, and neutral</sub> | <sub>Audio, Video</sub> | <sub>42 GB</sub> | <sub>English, Chinese, Spanish, Japanese</sub> | <sub>[A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670605.pdf)</sub> | <sub>Open</sub> | <sub>Non-commercial license</sub> |
| <sub>[Dusha](https://github.com/salute-developers/golos/tree/master/dusha)</sub> | <sub>2022</sub> | <sub> 300 000 audio recordings (~350 hours) of Russian speech, their transcripts and emotiomal labels. The dataset has two subsets: acted and real-life</sub> | <sub>4 emotions: angry, happy, sad and neutral. Arousal and valence metrics are also available.</sub> | <sub>Audio</sub> | <sub>58 GB</sub> | <sub>Russian</sub> | <sub>[Large Raw Emotional Dataset with Aggregation Mechanism](https://arxiv.org/abs/2212.12266)</sub> | <sub>Open</sub> | <sub>[Public license with attribution and conditions reserved](https://github.com/salute-developers/golos/blob/master/license/en_us.pdf)</sub> |
| <sub>[Dusha](https://github.com/salute-developers/golos/tree/master/dusha)</sub> | <sub>2022</sub> | <sub>300000 audio recordings (~350 hours) of Russian speech, their transcripts and emotiomal labels. The dataset has two subsets: acted and real-life</sub> | <sub>4 emotions: angry, happy, sad and neutral. Arousal and valence metrics are also available.</sub> | <sub>Audio</sub> | <sub>58 GB</sub> | <sub>Russian</sub> | <sub>[Large Raw Emotional Dataset with Aggregation Mechanism](https://arxiv.org/abs/2212.12266)</sub> | <sub>Open</sub> | <sub>[Public license with attribution and conditions reserved](https://github.com/salute-developers/golos/blob/master/license/en_us.pdf)</sub> |
| <sub>[MAFW](https://mafw-database.github.io/MAFW/)</sub> | <sub>2022</sub> | <sub>10045 video-audio clips in the wild.</sub> | <sub>11 single-label emotion categories (anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment) and 32 multi-label emotion categories.</sub> | <sub>Audio, Video</sub> | <sub>--</sub> | <sub>--</sub> | <sub>[MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild](https://arxiv.org/abs/2208.00847)</sub> | <sub>Restricted</sub> | <sub>Non-commercial research purposes</sub> |
| <sub>[EMOVIE](https://viem-ccy.github.io/EMOVIE/dataset_release.html)</sub> | <sub>2021</sub> | <sub>9724 samples with audio files and its emotion human-labeled annotation.</sub> | <sub>Polarity metrics (positive:+1, negative:-1)</sub> | <sub>Audio</sub> | <sub>0.572 GB</sub> | <sub>Chinese (Mandarin)</sub> | <sub>[EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model](https://arxiv.org/abs/2106.09317)</sub> | <sub>Open</sub> | <sub>[CC BY-NC-SA 2.0](https://creativecommons.org/licenses/by-nc-sa/2.0/legalcode)</sub> |
| <sub>[emoUERJ](https://zenodo.org/records/5427549)</sub> | <sub>2021</sub> | <sub>Ten sentences from eight actors, equally divided between genders, and they were free to choose the phrases for record audios in four emotions (377 audios). </sub> | <sub>happiness, anger, sadness or neutral</sub> | <sub>Audio</sub> | <sub>0.1051 GB</sub> | <sub>Portuguese (Brazilian)</sub> | <sub>--</sub> | <sub>Open</sub> | <sub>[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</sub> |
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2 changes: 1 addition & 1 deletion src/ser-datasets.csv
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Expand Up @@ -14,7 +14,7 @@ Dataset,Year,Content,Emotions,Format,Size,Language,Paper,Access,License
`MESD <https://data.mendeley.com/datasets/cy34mh68j9/5>`_,2022,864 audio files of single-word emotional utterances with Mexican cultural shaping.,"6 emotions provides single-word utterances for anger, disgust, fear, happiness, neutral, and sadness.",Audio,0.097 GB,Spanish (Mexican),`The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning <https://pubmed.ncbi.nlm.nih.gov/34891601/>`_,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
`SyntAct <https://zenodo.org/record/6573016#.ZAjy_9LMJpj>`_,2022,Synthesized database with 997 utterances of three basic emotions and neutral expression based on rule-based manipulation for a diphone synthesizer which we release to the public ,"6 emotions: angry, bored, happy, neutral, sad and scared",Audio,0.941 GB,German,`SyntAct: A Synthesized Database of Basic Emotions <http://felix.syntheticspeech.de/publications/synthetic_database.pdf>`_,Open,`CC BY-SA 4.0 <https://creativecommons.org/licenses/by/4.0>`_
`BEAT <https://drive.google.com/drive/folders/1EKuWH8q178QOtFUYaNohdkZbBHQYAmhL>`_,2022,"76-Hour and 30-Speaker of 4 different languages: English (60h), Chinese (12h), Spanish (2h) and Japanese (2h).","8 emotions: happiness, anger, disgust, sadness, contempt, surprise, fear, and neutral","Audio, Video",42 GB,"English, Chinese, Spanish, Japanese",`A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis <https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670605.pdf>`_,Open,Non-commercial license
`Dusha <https://github.com/salute-developers/golos/tree/master/dusha>`_,2022," 300 000 audio recordings (~350 hours) of Russian speech, their transcripts and emotiomal labels. The dataset has two subsets: acted and real-life","4 emotions: angry, happy, sad and neutral. Arousal and valence metrics are also available.",Audio,58 GB,Russian,`Large Raw Emotional Dataset with Aggregation Mechanism <https://arxiv.org/abs/2212.12266>`_,Open,`Public license with attribution and conditions reserved <https://github.com/salute-developers/golos/blob/master/license/en_us.pdf>`_
`Dusha <https://github.com/salute-developers/golos/tree/master/dusha>`_,2022,"300000 audio recordings (~350 hours) of Russian speech, their transcripts and emotiomal labels. The dataset has two subsets: acted and real-life","4 emotions: angry, happy, sad and neutral. Arousal and valence metrics are also available.",Audio,58 GB,Russian,`Large Raw Emotional Dataset with Aggregation Mechanism <https://arxiv.org/abs/2212.12266>`_,Open,`Public license with attribution and conditions reserved <https://github.com/salute-developers/golos/blob/master/license/en_us.pdf>`_
`MAFW <https://mafw-database.github.io/MAFW/>`_,2022,10045 video-audio clips in the wild.,"11 single-label emotion categories (anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment) and 32 multi-label emotion categories.","Audio, Video",--,--,"`MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild <https://arxiv.org/abs/2208.00847>`_",Restricted,Non-commercial research purposes
`EMOVIE <https://viem-ccy.github.io/EMOVIE/dataset_release.html>`_,2021,9724 samples with audio files and its emotion human-labeled annotation.,"Polarity metrics (positive:+1, negative:-1)",Audio,0.572 GB,Chinese (Mandarin),`EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model <https://arxiv.org/abs/2106.09317>`_,Open,`CC BY-NC-SA 2.0 <https://creativecommons.org/licenses/by-nc-sa/2.0/legalcode>`_
`emoUERJ <https://zenodo.org/records/5427549>`_,2021,"Ten sentences from eight actors, equally divided between genders, and they were free to choose the phrases for record audios in four emotions (377 audios). ","happiness, anger, sadness or neutral",Audio,0.1051 GB,Portuguese (Brazilian),--,Open,`CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_
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