A Matlab-Based Signal Processing Toolbox for the Characterization and Analysis of Musical Vibrato

Zhang M, Lu H, Ren G, Smith S, Beauchamp J, Bocko MF. A Matlab-Based Signal Processing Toolbox for the Characterization and Analysis of Musical Vibrato. J Audio Eng Soc. 2017;65(5):408-422. doi:10.17743/jaes.2017.0010

@article{Zhang2017_18734,
  author = {Zhang, Mingfeng and Lu, Hengwei and Ren, Gang and Smith, Sarah and Beauchamp, James and Bocko, Mark F},
  title = {{A Matlab-Based Signal Processing Toolbox for the Characterization and Analysis of Musical Vibrato}},
  journal = {Journal of the Audio Engineering Society},
  volume = {65},
  number = {5},
  pages = {408--422},
  year = {2017},
  month = may,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2017.0010},
  url = {https://doi.org/10.17743/jaes.2017.0010}
}

TY  - JOUR
TI  - A Matlab-Based Signal Processing Toolbox for the Characterization and Analysis of Musical Vibrato
AU  - Zhang, Mingfeng
AU  - Lu, Hengwei
AU  - Ren, Gang
AU  - Smith, Sarah
AU  - Beauchamp, James
AU  - Bocko, Mark F
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 65
IS  - 5
SP  - 408
EP  - 422
PY  - 2017
DA  - 2017/05/06
DO  - 10.17743/jaes.2017.0010
UR  - https://doi.org/10.17743/jaes.2017.0010
PB  - Audio Engineering Society
LA  - en
AB  - To assess and manipulate the vibrato in musical sounds, audio engineers either informally listen to the audio or visually inspect waveform envelopes or spectrographic representations. Unfortunately, detailed descriptions of the amplitude and frequency trajectories of harmonic partials are difficult to infer from audio spectrograms, which means quantitative information is limited. This paper describes a collection of signal processing methods and a toolbox for extracting and analyzing vibrato-related parameters from solo audio recordings. The Vibrato Analysis Toolbox (VAT) uses a method based on the Hilbert transform to extract the amplitude and frequency variations as feature tracks. A parameterization algorithm then extracts various descriptive parameters including vibrato depth, frequency, spectral centroid, relative amplitude-frequency modulation phase and time delay, and other relationships based on the vibrato tracks. Together, these parameters provide a quantitative characterization of vibrato. The VAT also provides visualization and resynthesis functions that enable users to interactively explore many musical features. Algorithms are written in the Matlab programming language for easy adaptation, enabling further development by researchers and developers. Applications include music performance pedagogy, musicological studies, music production, and voice analysis.
ER  -