dariahem / biovoice Goto Github PK

BioVoice is a user-friendly software platform designed to perform acoustical analyses of the human voice. BioVoice was developed at the Biomedical Engineering Lab, Università degli Studi di Firenze. It allows recording the human voice of newborns, children, adults, and singing voice, performing both time and frequency analysis, estimating more than 20 acoustical parameters. The user has to follow a few mandatory steps to perform voice analysis.

BioVoice is a software platform designed in MATLAB® and it requires MATLAB® Runtime R2017b. Here it executable beta version is available, that does not require the installation of the MATLAB® software.

The computer must be equipped with at least an Intel® Core™i3 processor with a 64-bit card. Windows OS (from Windows 7 on) is required. It does not run under Mac OS.

To run BioVoice, click on the green "clone or download" button on the top right side of the window. Save the BioVoice-master.zip file and unzip it. Launch BioVoice.exe and follow the instructions to install BioVoice BioVoice_Installing_Guide.pdf.

BioVoice is the exclusive property of Università degli Studi di Firenze, and is distributed free of charge on this platform. Any unauthorized use or its marketing will be legally prosecuted.

Please use the following references when you cite BioVoice:

1. Morelli, M. S., Orlandi S., Manfredi C. (2020). BioVoice: a Multipurpose Tool for Voice Analysis, Biomedical Signal Processing and Control

2. Morelli, M.S., Orlandi, S., Manfredi, C., BioVoice: a multipurpose tool for voice analysis, Proc. 11th Int. Workshop Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2019, December, 17-19, 2019, Firenze University Press, (2019) 261-264. Link

3. Rruqja, N., Dejonckere, P. H., Cantarella, G., Schoentgen, J., Orlandi, S., Barbagallo, S. D., & Manfredi, C. (2014). Testing software tools with synthesized deviant voices for medicolegal assessment of occupational dysphonia. Biomedical Signal Processing and Control, 13, 71-78. Link

4. Orlandi, S., Dejonckere, P. H., Schoentgen, J., Lebacq, J., Rruqja, N., & Manfredi, C. (2013). Effective pre-processing of long term noisy audio recordings: An aid to clinical monitoring. Biomedical Signal Processing and Control, 8(6), 799-810.Link

1. Manfredi, C., Viellevoye, R., Orlandi, S., Torres-García, A., Pieraccini, G., & Reyes-García, C. A. (2019). Automated analysis of newborn cry: relationships between melodic shapes and native language. Biomedical Signal Processing and Control, 53, 101561. Link
2. Manfredi, C., Bandini, A., Melino, D., Viellevoye, R., Kalenga, M., & Orlandi, S. (2018). Automated detection and classification of basic shapes of newborn cry melody. Biomedical Signal Processing and Control, 45, 174-181. Link
3. Orlandi, S., Bandini, A., Fiaschi, F. F., & Manfredi, C. (2017). Testing software tools for newborn cry analysis using synthetic signals. Biomedical Signal Processing and Control, 37, 16-22. Link
4. Orlandi, S., Garcia, C. A. R., Bandini, A., Donzelli, G., & Manfredi, C. (2016). Application of pattern recognition techniques to the classification of full-term and preterm infant cry. Journal of Voice, 30(6), 656-663. Link
5. Bandini, A., Giovannelli, F., Orlandi, S., Barbagallo, S. D., Cincotta, M., Vanni, P., ... & Manfredi, C. (2015). Automatic identification of dysprosody in idiopathic Parkinson's disease. Biomedical Signal Processing and Control, 17, 47-54. Link
6. Manfredi, C., Barbagallo, D., Baracca, G., Orlandi, S., Bandini, A., & Dejonckere, P. H. (2015). Automatic assessment of acoustic parameters of the singing voice: application to professional western operatic and jazz singers. Journal of Voice, 29(4), 517-e1. Link
7. Orlandi, S., Bocchi, L., Donzelli, G., & Manfredi, C. (2012). Central blood oxygen saturation vs crying in preterm newborns. Biomedical Signal Processing and Control, 7(1), 88-92. Link
8. Dejonckere, P. H., Giordano, A., Schoentgen, J., Fraj, S., Bocchi, L., & Manfredi, C. (2012). To what degree of voice perturbation are jitter measurements valid? A novel approach with synthesized vowels and visuo-perceptual pattern recognition. Biomedical Signal Processing and Control, 7(1), 37-42. Link
9. Manfredi, C., Giordano, A., Schoentgen, J., Fraj, S., Bocchi, L., & Dejonckere, P. H. (2012). Perturbation measurements in highly irregular voice signals: Performances/validity of analysis software tools. Biomedical signal processing and control, 7(4), 409-416. Link
10. DeJonckere, P., Schoentgen, J., Giordano, A., Fraj, S., Bocchi, L., & Manfredi, C. (2011). Validity of jitter measures in non-quasi-periodic voices. Part I: perceptual and computer performances in cycle pattern recognition. Logopedics Phoniatrics Vocology, 36(2), 70-77. Link
11. Manfredi, C., Giordano, A., Schoentgen, J., Fraj, S., Bocchi, L., & Dejonckere, P. (2011). Validity of jitter measures in non-quasi-periodic voices. Part II: The effect of noise. Logopedics Phoniatrics Vocology, 36(2), 78-89. Link
12. Manfredi, C., Bocchi, L., & Cantarella, G. (2009). A multipurpose user-friendly tool for voice analysis: application to pathological adult voices. Biomedical Signal Processing and Control, 4(3), 212-220. Link
13. Manfredi, C., Bocchi, L., Orlandi, S., Calisti, M., Spaccaterra, L., & Donzelli, G. P. (2008, August). Non-invasive distress evaluation in preterm newborn infants. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2908-2911). IEEE. Link