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MAuD: a multivariate audio database of samples collected from benchmark conferencing platforms

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Abstract

This paper presents an unique audio database, we named it Multivariate Audio Database (MAuD), where audio data has been collected in real life scenarios. MAuD contains 229 audio files, each of duration approx 5 minutes, collected across different conferencing apps, spoken languages, background noises and discussion topics. Various audio conferencing applications have been used for collecting these data e.g. Mobile conference calls, Zoom, Google Meet, Skype and Hangout. During this collection, speakers of different age, sex spoke in several languages and on various topics. Audio was recorded using devices of one of the speakers. Background noises were then introduced synthetically. Researchers may find this database useful as it can be used for several signal processing experiments e.g. conference app identification, background noise identification, speaker identification, identification of who speaks when. We have explored classification of some of the above mentioned mismatch cases (conference app and background noise). Pre-trained deep learning models (ResNet18 and DenseNet201) has been used for these purposes. We have achieved more than 98% accuracy in both the experiments that confirms MAuD contains high quality audio specific properties.

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Data Availability

Sample audio files can be found in the link [6] and detailed information about the database can be found in supplementary file named MAuD-DataDetails. Entire database will be made available for researchers after acceptance of this paper.

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Acknowledgements

This project is partially supported by the CMATER laboratory of the Computer Science and Engineering Department, Jadavpur University, India. We acknowledge the contribution of Ms Adrita Chakrabarti for assisting in the data collection process.

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Authors and Affiliations

  1. Jadavpur University, Kolkata, 700032, India

    Tapas Chakraborty, Rudrajit Bhattacharyya, Nibaran Das, Subhadip Basu & Mita Nasipuri

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  1. Tapas Chakraborty
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  2. Rudrajit Bhattacharyya
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  3. Nibaran Das
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  4. Subhadip Basu
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  5. Mita Nasipuri
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Correspondence to Tapas Chakraborty.

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Conflicts of interest

The authors state that there are no conflicts of interests. For this research work, We have not received any funding from any of these conferencing application providers. Our aim was to include all the frequently used conferencing applications without having any bias towards any corporate entity. We were not able to include some of the conferencing apps (for example Whats-App) as they do not have built-in recording features.

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Chakraborty, T., Bhattacharyya, R., Das, N. et al. MAuD: a multivariate audio database of samples collected from benchmark conferencing platforms. Multimed Tools Appl 83, 38465–38479 (2024). https://doi.org/10.1007/s11042-023-16879-5

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  • DOI: https://doi.org/10.1007/s11042-023-16879-5

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