Indoor and outdoor environmental data: A dataset with acoustic data acquired by the microphone embedded on mobile devices

All mobile devices include a microphone that can be used for acoustic data acquisition. This article presents a dataset of acoustic signals related to nine environments, captured with a microphone embedded on off-the-shelf mobile devices. The mobile phone can be placed in the pants pockets, in a wristband, over the bedside table, on a table, or on other furniture. Data collection environments are bar, classroom, gym, kitchen, library, street, hall, living room, and bedroom. The data was collected by 25 individuals (15 men and 10 women) in different environments around Covilhã and Fundão municipalities (Portugal). The microphone data was sampled with 44,100 Hz into an array with 16-bit unsigned integer values in the range [0, 255] with a 128 offset for zero. The dataset presented in this paper presents at least 2000 samples of 5 s of data for each environment, corresponding to around 2.8 h for each environment into text files. In total, it includes at least 25.2 h of acoustic data for the implementation of data processing techniques, e.g., Fast Fourier Transform (FFT), and other machine learning methods for the different analysis.

[1]  Nicholas D. Lane,et al.  DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning , 2015, UbiComp.

[2]  Vesa T. Peltonen,et al.  Audio-based context recognition , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[3]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[4]  Ivan Miguel Pires,et al.  Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults , 2020, Sensors.

[5]  Ivan Miguel Pires,et al.  Activities of daily living with motion: A dataset with accelerometer, magnetometer and gyroscope data from mobile devices , 2020, Data in brief.

[6]  Simone Orcioni,et al.  Digital Signal Processing for Audio Applications: Then, Now and the Future , 2019, The First Outstanding 50 Years of “Università Politecnica delle Marche”.

[7]  Nuno M. Garcia,et al.  Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study , 2020 .

[8]  Marc Green,et al.  Environmental sound monitoring using machine learning on mobile devices , 2020, Applied Acoustics.

[9]  Jana Dittmann,et al.  Digital audio forensics: a first practical evaluation on microphone and environment classification , 2007, MM&Sec.

[10]  Claudio Gentile,et al.  Microphone Identification Using Convolutional Neural Networks , 2019, IEEE Sensors Letters.

[11]  Eftim Zdravevski,et al.  From Big Data to business analytics: The case study of churn prediction , 2020, Appl. Soft Comput..

[12]  N. Garcia,et al.  Mobile Applications for the Promotion and Support of Healthy Nutrition and Physical Activity Habits: A Systematic Review, Extraction of Features and Taxonomy Proposal , 2019 .

[13]  James M. Rehg,et al.  Using Sound Source Localization in a Home Environment , 2005, Pervasive.

[14]  N. Garcia,et al.  Mobile Applications for the Promotion and Support of Healthy Nutrition and Physical Activity Habits: A Systematic Review, Extraction of Features and Taxonomy Proposal , 2019 .