Vocaliscope

An open-source MATLAB App Designer application for integrated analysis of multi-channel voice recordings.

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Overview

Vocaliscope is a unified environment for loading, synchronizing, processing, and analyzing multi-modal voice data. It supports heterogeneous data sources — acoustic signals, electroglottographic (EGG) measurements, and high-speed video-derived signals such as glottal area waveforms (GAW) and Phonovibrograms (PVG) — within a single, interactive interface.

The software consolidates functionality typically distributed across multiple tools, enabling reproducible multi-channel voice analysis without programming expertise.

Features

Data Import & Export

• Audio formats: .wav, .mp3, .flac, .ogg, .opus, .m4a, .mp4, .aifc, .aiff, .aif, .au
• Text/spreadsheet formats: .txt, .csv, .xlsx, .xls, .xlsm, .xlsb
• Glottal area waveforms: .gaw (Glottis Analysis Tools format [25])
• Phonovibrogram images: .png, .bmp, .jpg, .jpeg, .jp2
• Photron high-speed camera: .wvd, .csv (Type-A) [13]
• Session files: .mat (Vocaliscope format for resuming analysis)
• Export: .xlsx (results + settings), .wav (multi-channel, 32/64-bit), .mat (full session data)

Signal Processing

• Change source type: Switch channel type (Audio, EGG, Pressure, Flow, GAW, etc.) via right-click menu
• Unit conversion: Pa ↔ cmH₂O (temperature-dependent), dB ↔ Magnitude
• Basic editing: Set to constant values (zero, min/max/mean/median), add or subtract values, delete and concatenate, overwrite with linear ramp, differential operations (set extrema to zero), delete inverse selection, convert leading zeros to lag, padding (pre/post/both). Applies to single channel, all displayed channels, or all available channels
• Calibration: By absolute difference of start/end of marked region, by summed values inside marked region, by SPL inside marked region, or by comparison of two channels

Visualization

• Synchronized multi-channel display in time domain
• Time-frequency view via fast Continuous Wavelet Transform (fCWT [5])
• Interactive region selection with playback (diotic, selected channels only)
• Customizable channel colors (per source type, with color picker)
• Spectral view with logarithmic amplitude and peak information overlay

Channel Processing & Analysis Tools

All tools below are accessible via the right-click context menu on any channel axis.

Filter Toolbox

Interactive GUI with live preview in time and spectrum domains, plus filter response visualization (magnitude, phase delay, group delay) and audio preview (listen to filtered or original signal).

Non-spectral smoothing:

• Moving mean, Moving median
• Configurable window length
• Option to leave non-outliers untouched

IIR spectral filtering:

• Response types: lowpass, highpass, bandpass, bandstop, comb
• Design methods: Butterworth, Chebyshev Type I, Chebyshev Type II, Elliptic
• Full frequency specifications (passband/stopband frequencies)
• Full magnitude specifications (passband/stopband attenuation in dB)
• Forward and backward filtering option
• Configurable padding method
• Preserve zero values and clip below option

Resampling

Interactive GUI with before/after preview.

• Anti-aliasing resampling to a new sample rate, or time-vector-only mode (corrects wrong sample rate metadata without resampling the data — useful when e.g. a GAW file was saved at 100 kHz but the actual data is 20 kHz)
• Interpolation methods: Linear, Shape-preserving piecewise cubic, Spline
• Scope: single channel, all channels, or all channels above a specified sample rate threshold

Praat Playground

Comprehensive Praat analysis window via praatMEX [21] with full parameter control and live preview. Eight analysis categories:

• Pitch: Filtered/unfiltered cross-correlation, autocorrelation, SHS, SPINET — with voicing thresholds, octave costs, very accurate mode, fill-missing interpolation
• Formant: Burg, Keep all, Willems, Robust methods — with formant tracking, max formants, pre-emphasis
• Intensity: Min pitch, window length, subtract mean
• Harmonicity/HNR/GNE: Time step, pitch floor, silence threshold, periods per window (HNR); min/max frequency, bandwidth (GNE)
• Jitter: Absolute, local, local absolute, relative, RAP, PPQ5, ddp — with configurable period limits and window settings
• Shimmer: Absolute, local, local absolute, relative, APQ5, apq11, dda — with configurable amplitude factor and window settings
• CPP/CPPS: Pre-emphasis, trend removal, quefrency smoothing, peak interpolation (parabolic, cubic, sinc), robust fitting
• PointProcess: Periodic CC, periodic peaks, extrema, periodic direct methods

Creates one or more new channels per analysis type.

SPL Channel Creation

Creates a new Sound Pressure Level channel from an audio signal using MATLAB's splMeter.

• Weighting: dB(A), dB(C), dB(Z)
• Time constant: Fast (125 ms) or Slow (1 s)
• Reference pressure: User-configurable (default: 20 µPa)
• Output metrics: Time-weighted sound level, Equivalent-continuous sound level (LEQ), Peak sound level, Maximum time-weighted sound level
• Report interval: Configurable integration interval for LEQ/max metrics
• Live preview of the SPL curve

Fundamental Frequency Channel

Creates a new f₀ contour channel with selectable pitch estimation algorithm. 17+ methods available:

• MATLAB built-in: NCF, PEF, CEP, LHS, SRH
• Third-party: SWIPE' [8], SHR [6], YIN [7], Bayesian F0 (BF0NLS) [9], YAAPT [10]
• Deep learning: CREPE [27] (DNN capacity: tiny/small/medium/large/full)
• Praat-based: Filtered AC, raw AC, raw CC, filtered CC, SHS, SPINET — each with full Praat parameter control (voicing threshold, octave costs, very accurate mode, etc.)

Configurable min/max f₀, window length, overlap, and missing-data interpolation method.

Formant Estimation

Creates one or more new formant channels with CWT scalogram background and overlaid formant tracks.

• TVQCP [11]: Time-Varying Quasi Closed Phase linear prediction — with L2-norm weighted or unweighted LP
• Story & Bunton [12]: Formant estimation adapted for pediatric voice analysis

Configurable number of formants (1–5), f₀ range, window length, and overlap.

Sample Entropy (Fourier Descriptors)

Interactive standalone app for computing per-cycle Sample Entropy from EGG signals based on Fourier Descriptors [23].

• Glottal cycle detection via Hilbert phase analysis combined with dEGG peak detection
• Configurable f₀ range, dEGG and Hilbert detection thresholds, phase/magnitude thresholds
• Adjustable number of cycles per SampEn calculation
• Option to export instantaneous cycle frequency as an additional channel

Nasalance

Computes nasalance (ratio of nasal to total airflow) from two flow-signal channels.

• Select nasal and oral flow channels from existing data
• Processing methods: Butterworth lowpass filtering or dB(Z)-weighted Moving RMS
• Configurable passband/stopband frequencies and stopband attenuation
• Live 6-panel preview (raw and processed nasal, oral, and nasalance)

Mask Creation

Creates a binary mask channel (0/1) from a signal based on amplitude extrema detection.

• Amplitude range slider (0–100%) to define active regions between detected valleys and peaks
• Configurable maximum frequency and minimum extrema prominence
• Persistent settings across sessions

Voice Activity Probability

Creates a voice activity probability channel (0–1) using MATLAB's voiceActivityDetector.

• Frame window types: Flat-top, Hamming, Hann, Kaiser, Chebyshev, Rectangular
• Configurable frame length, overlap, speech-to-silence and silence-to-speech transition probabilities
• Live preview plot

Envelope

Creates amplitude envelope channels (upper, lower, or both) from any signal. 13 methods available:

• Analytic (Hilbert transform), Peak-based
• Moving statistics: RMS, min/max, abs. max, mean, median, STD — each with variants for values above/below a reference
• Configurable center reference method (moving/global mean/median), window length, and window direction (centered/forward/backward)
• Creates one or two new channels depending on envelope selection

Vibrato Analysis

Four methods available:

• VAT — Hilbert AM/FM via Vibrato Toolbox [20]
• AVA — FDM-based Auditory Vibrato Analysis [18]
• VibratoScope — Cycle-based analysis [19]
• CWTf₀ — Continuous Wavelet Transform of f₀ contour (custom implementation using fCWT)

Features: pitch contour + vibrato markers preview, speed/extent plots, method descriptions with references, CSV export, persistent settings.

Onset Analysis

Four methods available:

• VRT — Vocal Rise Time, energy VAD based (original implementation)
• POT-Mergell — Phonation Onset Time (original implementation)
• POT-Petermann — Phonation Onset Time (original implementation)
• VAT — Voice Attack Time, dEGG-based with EGG+audio (original implementation)

Features: waveform + onset marker preview, intermediate results display, multi-channel comparison, CSV export, persistent settings.

Inverse Filtering

• Aalto Aparat — QCP and IAIF methods [14]
• GFM-IAIF [16], IOP-IAIF [17], MCLP [33] (with OLAF variants [16], [17])
• Configurable vocal tract order, glottal source order, window length, overlap, leaky integration coefficient

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Voice Parameters

Vocaliscope computes approximately 65 active voice parameters across multiple categories, organized in a tree structure. Parameters are computed from user-defined segments and exported to .xlsx. The parameter set largely follows the Glottis Analysis Tool (GAT) [25]; the source of each formula is cited in the corresponding category below.

Fundamental Frequency Measures

• Fundamental Frequency (Hz)
• Cycle Duration (s)
• Maximum Harmonic (Hz)
• Minimum Subharmonic (Hz)

Amplitude Perturbation Measures

• Mean Shimmer (dB) [36] [37] [42]
• Shimmer (%) [37] [38]
• Amplitude Perturbation Quotient (APQ) [39] [40] [41]
• Amplitude Perturbation Factor (APF) [39] [40] [41]
• Amplitude Variability Index (AVI) [46]

Period Perturbation Measures

• Mean Jitter (s) [36] [40]
• Jitter (%) [36] [43]
• Jitter Ratio [43]
• Jitter Factor [44]
• Period Perturbation Quotient (PPQ) [37] [38] [39] [40] [41]
• Period Perturbation Factor (PPF) [39] [40] [41]
• Relative Average Perturbation V1 (RAP) [40] [41] [45]
• Relative Average Perturbation V2 (RAP) [40] [41] [45]
• Period Variability Index (PVI) [38] [40]

Energy Perturbation Measures

• Energy Perturbation Quotient (EPQ) [39] [40] [41]
• Energy Perturbation Factor (EPF) [39] [40] [41]

Noise Measures

• Spectral Flatness [47] [61]
• Cepstral Peak Prominence I (CPP)

Glottal Area Measures

• Open Quotient (OQ) [36] [49]
• Closing Quotient (ClQ) [50]
• Speed Quotient (SQ) [36]
• Speed Index (SI) [36]
• Rate Quotient [36]
• Asymmetry Quotient [51]
• Glottis Gap Index (GGI) [52] [53]
• Plateau Quotient [54]
• Glottal Area Index (GAI) [55]

Glottal Area Periodicity Measures

• Amplitude-Periodicity [56]
• Time-Periodicity [56]

Glottal Area Derivative Measures

• Maximum Area Declination Rate (MADR) [50] [57] [58] [59]
• Amplitude Quotient [57] [59]

Basic Measurements

• Sum [69]
• Mean (arithmetic, harmonic, geometric) [69]
• Standard Deviation (STD) [69]
• Median [69]
• Inter-Quartile Range (IQR) [69]
• Minimum / Maximum [69]
• Root Mean Square (RMS) [69]
• Mode [69]
• Quantiles (5%–95% in 5% steps) [69]
• Skewness [68]
• Kurtosis [68]
• Sum of Square Values [69]

Additional Spectral Descriptors

• Spectral Crest [60]
• Spectral Flux [60]
• Spectral Spread [60]
• Spectral Skewness (Spectral Tilt) [60]
• Spectral Kurtosis [60]
• Spectral Centroid [60]
• Spectral Entropy [60]
• Spectral Slope [60]
• Spectral Decrease [60]
• Spectral Rolloff Point [60]
• Harmonic Ratio [60]
• Harmonic-to-Noise Ratio (HNR) via cross-correlation [48]
• Signal-to-Noise Ratio (SNR, dB carrier) [62]

Psychoacoustic Measures

• Acoustic Loudness (ISO 532-1 stationary loudness) [64]
• Acoustic Fluctuation [66]
• Acoustic Sharpness (DIN 45692) [65]
• Acoustic Roughness [67]

Others

• Zero Crossing Rate [60]
• Sample Entropy (time domain) [23]
• Cycle-based Spectral Sample Entropy [23]
• Sound Pressure Level (SPL: dB(A), dB(C), dB(Z)) [63]
• RAP for cycle energy [40] [41] [45]
• RAP for cycle range [40] [41] [45]
• Energy Variability Index (EVI) [46]
• Glottal-to-Noise Excitation Ratio (GNE) v2 [22]
• Cepstral Peak Prominence Smoothed v3 (CPP(s))

Planned Parameters (not yet available)

The following parameters are planned for a future release and are not yet available:

Mechanical Measures:

• Stiffness (1/s)
• Peak Closing Velocity (px/s)
• Peak Acceleration (px/s²)
• Amplitude-to-Length Ratio

Glottal Area Symmetry Measures:

• Phase Asymmetry Index
• Spatial Symmetry Index
• Dynamic Range Symmetry Index
• Amplitude Symmetry Index
• Waveform Symmetry Index
• Phase Asymmetry
• Spatial Symmetry
• Dynamic Range Symmetry
• Amplitude Symmetry

Additional Noise Measures:

• Glottal-to-Noise Excitation Ratio (GNE) v1
• Harmonics Intensity
• Harmonics-to-Noise Ratio (dB) (original implementation)
• Normalized Noise Energy (NNE)
• Waveform Matching Coefficient (WMC)
• Mean Waveform Matching Coefficient
• Signal-to-Noise Ratio V1 (dB)
• Signal-to-Noise Ratio V2 (dB)
• Cepstral Peak Prominence II (dB)

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Pitch (Tracking) Algorithms

Vocaliscope includes multiple pitch (tracking) algorithms available in parameter calculation and/or via right-click menu:

Algorithm	Parameter Calculation	Right-Click Menu	Reference
NCF — Normalized Correlation Function (default)	✓	✓	[28] · MATLAB docs
PEF — Pitch Estimation Filter	✓	✓	[29] · MATLAB docs
CEP — Cepstrum Pitch Determination	✓	✓	[30] · MATLAB docs
LHS — Log-Harmonic Summation	✓	✓	[31] · MATLAB docs
SRH — Summation of Residual Harmonics	✓	✓	[32] · MATLAB docs
SHR (Subharmonic)	✓	✓	[6]
YIN	—	✓	[7]
SWIPE'	✓	✓	[8]
Bayesian F0 (BF0NLS)	—	✓	[9]
YAAPT	—	✓	[10]
CREPE (deep neural network)	—	✓	[27]
Praat: filtered cross-correlation	—	✓	[1]
Praat: unfiltered cross-correlation	—	✓	[1]
Praat: autocorrelation	—	✓	[1]
Praat: SHS	—	✓	[1]
Praat: SPINET	—	✓	[1]

The NCF, PEF, CEP, LHS, and SRH methods are implemented via MATLAB's built-in pitch() function (Audio Toolbox). The CREPE method is implemented via MATLAB's pitchnn() function (Audio Toolbox + Deep Learning Toolbox); the pretrained network .mat files are downloaded automatically on first use (see [27]). Praat-based methods are implemented via praatMEX [21] — a C++ MEX function that links the Praat core as a static library.

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Installation

Option A: Compiled executable (no MATLAB license required)

1. Download the .exe file and all folders except forCompiling/
2. Install MATLAB Runtime version 2026a
3. Run Vocaliscope_V5_0.exe

Note: The compiled executable includes all dependencies (fCWT, praatMEX, inverse filtering methods, etc.).

Option B: MATLAB App (requires MATLAB license)

1. Download only the forCompiling/ folder
2. Open in MATLAB R2026a or later
3. Open Vocaliscope.mlapp from the Current Folder

Note: The .mlapp version is generally more performant and allows direct code inspection.

Additional Requirements

• MATLAB toolboxes (for .mlapp usage): Signal Processing Toolbox, Audio Toolbox (for some features)

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Dependencies & Acknowledgements

Vocaliscope builds upon and includes code from the following open-source tools and libraries:

Pitch Tracking

#	Dependency	Description	Repository / Source
[6]	SHR	Subharmonic pitch determination algorithm	MathWorks File Exchange
[7]	YIN	YIN pitch estimator (derived from AaltoAparat)	mhamilt/yin
[8]	SWIPE'	Sawtooth waveform inspired pitch estimation	kylebgorman/swipe
[9]	Bayesian F0	Bayesian pitch tracking using harmonic model	LimingShi/Bayesian-Pitch-Tracking
[10]	YAAPT	Yet Another Algorithm for Pitch Tracking	Binghamton YAAPT page · Zahorian & Hu, JASA, 123(6), 4559–4571 (2008). DOI: 10.1121/1.2916590
[27]	CREPE	Deep neural network pitch estimation (via MATLAB pitchnn)	MATLAB pitchnn docs · CREPE model (.mat)

Formant Estimation

#	Dependency	Description	Repository / Source
[11]	ftrack / TVQCP	Time-varying linear prediction formant estimation	njaygowda/ftrack
[12]	Story & Bunton	Formant estimation for children	TrackChildFmts.m (Arizona SAPL)
[26]	GLOAT / SEDREAMS	GCI detection (used by TVQCP)	covarep/covarep (hosts gci_sedreams.m)

Inverse Filtering

#	Dependency	Description	Repository / Source
[14]	Aalto Aparat	Inverse filtering toolkit (QCP, IAIF)	Aalto University
[15]	GFM-IAIF	Glottal source estimation	operrotin/GFM-IAIF
[16]	GFM-IAIF (OLAF variant)	Extended glottal source estimation	SpeechSalleBcn/inverse-filtering-evaluation
[17]	IOP-IAIF (OLAF variant)	Glottal source estimation	SpeechSalleBcn/inverse-filtering-evaluation
[33]	MCLP	Maximum Correntropy Linear Prediction	Izalazar/MCLP

Vibrato Analysis

#	Dependency	Description	Repository / Source
[18]	AVA	Automatic Vibrato Analysis (FDM-based)	skx300/ava
[19]	VibratoScope	Cycle-based vibrato analysis	tiagolbc/vibratoscope
[20]	Vibrato Toolbox	Hilbert AM/FM vibrato analysis	sourceforge.net/projects/vibrato · Zhang et al., J. Audio Eng. Soc., 65(5), 408–422 (2017). DOI: 10.17743/jaes.2017.0010

Noise & Nonlinear Analysis

#	Dependency	Description	Repository / Source
[22]	GNE / TKEO	Glottal-to-Noise Excitation Ratio	Thanasis Tsanas, Voice Analysis Toolbox
[23]	fastSampEn	Fast Sample Entropy algorithm	File Exchange #48574

Signal Processing & Visualization

#	Dependency	Description	Repository / Source
[5]	fCWT	Fast Continuous Wavelet Transform	fastlib/fCWT

Praat Integration

#	Dependency	Description	Repository / Source
[1]	Praat	Phonetics by computer — speech analysis and synthesis	praat/praat
[21]	praatMEX	In-process Praat C++ API via MEX	jkirsch-code/praat-mex

Other

#	Dependency	Description	Repository / Source
[13]	Photron FASTCAM Viewer (PFV)	High-speed camera data format	Photron Limited
[25]	Glottis Analysis Tool (GAT)	GCI and glottal analysis (C#/.NET)	Kist et al., J. Speech Lang. Hear. Res. (2021). DOI: 10.1044/2021_JSLHR-20-00498 · GAT software (on request)
[34]	SUBDIR	Recursive directory listing utility	File Exchange #15859 · kakearney/subdir-pkg

Related Tools (referenced but no code included)

The following tools are related to Vocaliscope's domain but their code is not included in this repository: Praat [1], VoiceSauce [2], Sopran [3], VoiceLab [4], Audacity [24], COVAREP [35] (referenced in inverse filtering method attributions).

References

#	Reference	URL / DOI
[1]	Boersma, P. & Weenink, D. — Praat: doing phonetics by computer	praat.org · Boersma & van Heuven, Glot International, 5(9/10), 1–7 (2001)
[2]	Shue, Y.-L., Keating, P., Vicenik, C. & Yu, K. — VoiceSauce	phonetics.ucla.edu · Shue et al., Proc. ICPhS XVII, 1846–1849 (2011)
[3]	Granqvist, S. — Sopran: a collection of soprano voice ranges	tolvan.com
[4]	Feinberg, D., Cook, O. & Yassine, E.K. — VoiceLab: Automated reproducible acoustic analysis	Voice-Lab/VoiceLab · Feinberg & Cook, PsyArXiv (2020). DOI: 10.31234/osf.io/v5uxf
[5]	Arts, L.P.A. & van den Broek, E.L. — fCWT: Fast Continuous Wavelet Transform	fastlib/fCWT · Arts & van den Broek, Nature Computational Science, 2, 47–58 (2022). DOI: 10.1038/s43588-021-00183-z
[6]	Sun, X. — Subharmonic pitch determination algorithm	File Exchange #1230 · ICSLP 2000, Beijing
[7]	de Cheveigné, A. & Kawahara, H. — YIN, a fundamental frequency estimator	mhamilt/yin · de Cheveigné & Kawahara, JASA, 111(4), 1917–1930 (2002). DOI: 10.1121/1.1458024
[8]	Camacho, A. & Harris, J.G. — SWIPE': A sawtooth waveform inspired pitch estimator	kylebgorman/swipe · Camacho & Harris, JASA, 124(3), 1638–1652 (2008). DOI: 10.1121/1.2951592
[9]	Shi, L., Wu, D. & Birkholz, P. — Bayesian pitch tracking using harmonic model	LimingShi/Bayesian-Pitch-Tracking · Shi et al., IEEE/ACM TASLP, 27(11), 1737–1751 (2019). DOI: 10.1109/TASLP.2019.2930917
[10]	Zahorian, S.A. & Hu, H. — YAAPT: An algorithm for pitch tracking	Binghamton YAAPT page · JASA 123(6), 4559–4571 (2008). DOI: 10.1121/1.2916590
[11]	Gowda, D., Kadiri, S.R., Story, B. & Alku, P. — ftrack / TVQCP: Time-varying quasi-closed-phase formant estimation	njaygowda/ftrack · IEEE/ACM TASLP, 28, 1901–1914 (2020). DOI: 10.1109/TASLP.2020.3000037
[12]	Story, B.H. & Bunton, K. — Formant estimation based on spectral filtering	Speech Communication, 76, 93–111 (2016). DOI: 10.1016/j.specom.2015.11.001 · Code: TrackChildFmts.m (Arizona SAPL)
[13]	Photron Limited — FASTCAM Viewer (PFV)	photron.com/pfv
[14]	Alku, P., Pohjalainen, H. & Airaksinen, M. — Aalto Aparat: Inverse filtering toolkit	research.spa.aalto.fi · Alku, Pohjalainen & Airaksinen, Subsidia: Tools and Resources for Speech Sciences, 21, p. 23 (2017)
[15]	Perrotin, O. & McLoughlin, I.V. — GFM-IAIF glottal source estimation	operrotin/GFM-IAIF · Perrotin & McLoughlin, Proc. ICASSP, 7160–7164 (2019). DOI: 10.1109/ICASSP.2019.8682625
[16]	Freixes, M. et al. — GFM-IAIF (OLAF variant) glottal source estimation	SpeechSalleBcn/inverse-filtering-evaluation · Applied Sciences 13(15), 8775 (2023). DOI: 10.3390/app13158775
[17]	Freixes, M. et al. — IOP-IAIF (OLAF variant) glottal source estimation	SpeechSalleBcn/inverse-filtering-evaluation · Applied Sciences 13(15), 8775 (2023). DOI: 10.3390/app13158775
[18]	Yang, L., Rajab, K.Z. & Chew, E. — AVA: Automatic Vibrato Analysis	skx300/ava · ISMIR 2016 PDF
[19]	Cruz, T.L.B. — VibratoScope: Cycle-based vibrato analysis	tiagolbc/vibratoscope · Zenodo (2025). DOI: 10.5281/zenodo.15519845
[20]	Zhang, M. et al. — Vibrato Toolbox: Hilbert AM/FM analysis	sourceforge.net/projects/vibrato · Zhang et al., J. Audio Eng. Soc., 65(5), 408–422 (2017). DOI: 10.17743/jaes.2017.0010
[21]	Kirsch, J. — praatMEX: In-process Praat C++ API via MEX	jkirsch-code/praat-mex
[22]	Tsanas, A. — GNE / TKEO (Voice Analysis Toolbox)	ThanasisTsanas/VoiceAnalysisToolbox · Tsanas, Proc. MAVEBA, 37–40 (2013)
[23]	Paa, F. — fastSampEn: Fast Sample Entropy algorithm	File Exchange #48574 · Richman & Moorman, Am. J. Physiol. Heart Circ. Physiol., 278(6), H2039–H2049 (2000). DOI: 10.1152/ajpheart.2000.278.6.H2039
[24]	Audacity Team — Audacity audio editor	audacityteam.org
[25]	Kist, A.M. et al. — Glottis Analysis Tool (GAT)	DOI: 10.1044/2021_JSLHR-20-00498 · Code: GAT software (on request)
[26]	Drugman, T. — GLOAT / SEDREAMS: Glottal closure instant detection	covarep/covarep (hosts gci_sedreams.m) · Drugman et al., IEEE/ACM TASLP, 20(3), 994–1006 (2012). DOI: 10.1109/TASL.2011.2170835
[27]	Kim, J.W., Salamon, J., Li, P. & Bello, J.P. — CREPE: A convolutional representation for pitch estimation	MATLAB pitchnn docs · CREPE model (.mat) · Kim et al., Proc. ICASSP, 161–165 (2018). DOI: 10.1109/ICASSP.2018.8461329
[28]	Atal, B.S. — Automatic speaker recognition based on pitch contours	J. Acoust. Soc. Am., 52(6B), 1687–1697 (1972). DOI: 10.1121/1.1913303 · MATLAB docs
[29]	Gonzalez, S. & Brookes, M. — A pitch estimation filter robust to high levels of noise (PEFAC)	EUSIPCO 2011 PDF · IEEE/ACM TASLP, 22(2), 518–530 (2014). DOI: 10.1109/TASLP.2013.2295918 · MATLAB docs
[30]	Noll, A.M. — Cepstrum pitch determination	J. Acoust. Soc. Am., 41(2), 293–309 (1967). DOI: 10.1121/1.1910339 · MATLAB docs
[31]	Hermes, D.J. — Measurement of pitch by subharmonic summation	J. Acoust. Soc. Am., 83(1), 257–264 (1988). DOI: 10.1121/1.396427 · MATLAB docs
[32]	Drugman, T. & Alwan, A. — Joint robust voicing detection and pitch estimation based on residual harmonics	Proc. INTERSPEECH, 1973–1976 (2011). DOI: 10.21437/Interspeech.2011-519 · MATLAB docs
[33]	Zalazar, A.L. et al. — MCLP: Maximum Correntropy Linear Prediction	Izalazar/MCLP · IEEE/ACM TASLP, 33, 152–162 (2025). DOI: 10.1109/TASLP.2024.3512187
[34]	Kearney, K. — subdir: Recursive directory listing utility	File Exchange #15859 · kakearney/subdir-pkg
[35]	Degottex, G., Kane, J., Drugman, T., Raitio, T. & Scherer, S. — COVAREP: A collaborative voice analysis repository for speech technologies	covarep/covarep · Proc. ICASSP, 960–964 (2014). DOI: 10.1109/ICASSP.2014.6853739
[36]	Baken, R.J. & Orlikoff, R.F. — Clinical Measurement of Speech and Voice (2nd ed.)	Open Library (ISBN 978-1-56593-869-4) · Baken & Orlikoff, Cengage Learning (1999)
[37]	Bielamowicz, S., Kreiman, J., Gerratt, B.R., Dauer, M.K. & Berke, G.S. — Comparison of voice analysis systems for perturbation measurement	Bielamowicz et al., J. Speech Lang. Hear. Res., 39(1), 126–134 (1996). DOI: 10.1044/jshr.3901.126
[38]	Titze, I.R., Horii, Y. & Scherer, S.R. — Some technical considerations in voice perturbation measurements	Titze, Horii & Scherer, J. Speech Lang. Hear. Res., 30(2), 252–260 (1987). DOI: 10.1044/jshr.3002.252
[39]	Kasuya, H., Endo, Y. & Saliu, S. — Novel acoustic measurements of jitter and shimmer characteristics from pathological voice	Kasuya, Endo & Saliu, Proc. EUROSPEECH'93, 1973–1976 (1993). DOI: 10.21437/eurospeech.1993-446
[40]	Lessing, J. — Entwicklung einer Klassifikationsmethode zur akustischen Analyse fortlaufender Sprache unterschiedlicher Stimmgüte mittels Neuronaler Netze und deren Anwendung [PhD thesis]	Lessing, PhD thesis, Georg-August-Universität Göttingen (2007). DOI: 10.53846/goediss-2873
[41]	Michaelis, D. — Das Göttinger Heiserheits-Diagramm: Entwicklung und Prüfung eines akustischen Verfahrens zur objektiven Stimmgütebeurteilung pathologischer Stimmen [PhD thesis]	Michaelis, PhD thesis, Georg-August-Universität Göttingen (2000). DOI: 10.53846/goediss-4239
[42]	Horii, Y. — Vocal shimmer in sustained phonation	Horii, J. Speech Lang. Hear. Res., 23(1), 202–209 (1980). DOI: 10.1044/jshr.2301.202
[43]	Horii, Y. — Fundamental frequency perturbation observed in sustained phonation	Horii, J. Speech Lang. Hear. Res., 22(1), 5–19 (1979). DOI: 10.1044/jshr.2201.05
[44]	Hollien, H., Michel, J. & Doherty, E.T. — A method for analyzing vocal jitter in sustained phonation	Hollien, Michel & Doherty, J. Phonetics, 1(1), 85–91 (1973). DOI: 10.1016/S0095-4470(19)31377-4
[45]	Koike, Y. — Application of some acoustic measures for the evaluation of laryngeal dysfunction	Koike, Studia Phonologica, 7, 17–23 (1973)
[46]	Deal, R.E. & Emanuel, F.W. — Some waveform and spectral features of vowel roughness	Deal & Emanuel, J. Speech Hear. Res., 21(2), 250–264 (1978). DOI: 10.1044/jshr.2102.250
[47]	Johnston, J.D. — Transform coding of audio signals using perceptual noise criteria	Johnston, IEEE J. Select. Areas Commun., 6(2), 314–323 (1988). DOI: 10.1109/49.608
[48]	Yumoto, E., Gould, W.J. & Baer, T. — Harmonics-to-noise ratio as an index of the degree of hoarseness	Yumoto, Gould & Baer, J. Acoust. Soc. Am., 71(6), 1544–1550 (1982). DOI: 10.1121/1.387808
[49]	Timcke, R., von Leden, H. & Moore, P. — Laryngeal vibrations: Measurements of the glottic wave. Part I. The normal vibratory cycle	Timcke, von Leden & Moore, Arch. Otolaryngol., 68(1), 1–19 (1958). DOI: 10.1001/archotol.1958.00730020005001
[50]	Holmberg, E.B., Hillman, R.E. & Perkell, J.S. — Glottal airflow and transglottal air pressure measurements for male and female speakers in soft, normal, and loud voice	Holmberg, Hillman & Perkell, J. Acoust. Soc. Am., 84(2), 511–529 (1988). DOI: 10.1121/1.396829
[51]	Henrich, N., Sundin, G., Ambroise, D., d'Alessandro, C., Castellengo, M. & Doval, B. — Just noticeable differences of open quotient and asymmetry coefficient in singing voice	Henrich et al., J. Voice, 17(4), 481–494 (2003). DOI: 10.1067/S0892-1997(03)00005-5
[52]	Patel, R., Dubrovskiy, D. & Döllinger, M. — Characterizing vibratory kinematics in children and adults with high-speed digital imaging	Patel, Dubrovskiy & Döllinger, J. Speech Lang. Hear. Res., 57(2) (2014). DOI: 10.1044/2014_JSLHR-S-12-0278
[53]	Kunduk, M., Döllinger, M., McWhorter, A.J. & Lohscheller, J. — Assessment of the variability of vocal fold dynamics within and between recordings with high-speed imaging and by phonovibrogram	Kunduk et al., Laryngoscope, 120(5), 981–987 (2010). DOI: 10.1002/lary.20832
[54]	Mehta, D.D., Zañartu, M., Quatieri, T.F., Deliyski, D.D. & Hillman, R.E. — Investigating acoustic correlates of human vocal fold vibratory phase asymmetry through modeling and laryngeal high-speed videoendoscopy	Mehta et al., J. Acoust. Soc. Am., 130(6), 3999–4009 (2011). DOI: 10.1121/1.3658441
[55]	Chen, G., Kreiman, J., Gerratt, B.R., Neubauer, R.T., Shue, H. & Alwan, A. — Development of a glottal area index that integrates glottal gap size and open quotient	Chen et al., J. Acoust. Soc. Am., 133(3), 1656–1666 (2013). DOI: 10.1121/1.4789931
[56]	Qiu, Q., Schutte, H.K., Gu, L. & Yu, Q. — An automatic method to quantify the vibration properties of human vocal folds via videokymography	Qiu et al., Folia Phoniatr. Logop., 55(3), 128–136 (2003). DOI: 10.1159/000070724
[57]	Alku, P., Airas, M., Björkner, E. & Sundberg, J. — An amplitude quotient based method to analyze changes in the shape of the glottal pulse in the regulation of vocal intensity	Alku et al., J. Acoust. Soc. Am., 120(2), 1052–1062 (2006). DOI: 10.1121/1.2211589
[58]	Titze, I.R. — Theoretical analysis of maximum flow declination rate versus maximum area declination rate in phonation	Titze, J. Speech Lang. Hear. Res., 49(2), 439–447 (2006). DOI: 10.1044/1092-4388(2006/034)
[59]	Patel, R., Dubrovskiy, D. & Döllinger, M. — Measurement of glottal cycle characteristics between children and adults: Physiological variations	Patel, Dubrovskiy & Döllinger, J. Voice, 28(4), 476–486 (2014). DOI: 10.1016/j.jvoice.2013.12.010
[60]	MathWorks — audioFeatureExtractor: Audio feature extraction	MATLAB docs (Audio Toolbox)
[61]	MathWorks — spectralFlatness: Spectral flatness for signals and spectrograms	MATLAB docs (Signal Processing Toolbox)
[62]	MathWorks — snr: Signal-to-noise ratio	MATLAB docs (Signal Processing Toolbox)
[63]	MathWorks — splMeter: Sound pressure level meter	MATLAB docs (Audio Toolbox)
[64]	MathWorks — acousticLoudness: Permanent and short-term loudness	MATLAB docs (Audio Toolbox; ISO 532-1 stationary loudness)
[65]	MathWorks — acousticSharpness: Sharpness sensation of a sound	MATLAB docs (Audio Toolbox; DIN 45692, Aures and von Bismarck models)
[66]	MathWorks — acousticFluctuation: Fluctuation strength	MATLAB docs (Audio Toolbox)
[67]	MathWorks — acousticRoughness: Roughness sensation of a sound	MATLAB docs (Audio Toolbox)
[68]	MathWorks — skewness and kurtosis: Sample skewness and kurtosis	skewness · kurtosis (Statistics and Machine Learning Toolbox)
[69]	MathWorks — Descriptive statistics: sum, mean, median, std, var, min, max, mode, rms, iqr, quantile	mean · std · mode · quantile (MATLAB)

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Citation

If you use Vocaliscope in your research, please cite:

@software{kirsch2026vocaliscope,
  author       = {Kirsch, Jonas},
  title        = {Vocaliscope: An open-source app for multi-channel voice research},
  year         = {2026},
  version      = {5.1},
  url          = {https://github.com/jkirsch-code/Vocaliscope},
  license      = {GPL-3.0-or-later}
}

See CITATION.cff for machine-readable citation metadata.

Studies using Vocaliscope

• Hoppermann, R., Kirsch, J., Pilsl, T., Köberlein, M., Döllinger, M., Echternach, M., 2026. Influence of a Whisper Loading Test on the Vibration Mechanism of the Vocal Folds in the Context of Organic Dysphonia. JCM 15, 1735. https://doi.org/10.3390/jcm15051735
• Kainz, M.-A., Hoppermann, R., Pilsl, T., Köberlein, M., Kirsch, J., Döllinger, M., Echternach, M., 2026. Effects of Bubbles During Water Resistance Therapy on the Vibration Characteristics of Vocal Folds During the Phonation of Different Vowels. JCM 15, 669. https://doi.org/10.3390/jcm15020669
• Kirsch, J., Köberlein, M., Döllinger, M., Echternach, M., 2026. Influence of Execution Speed of an Ascending Glissando on Vocal Stability of Vocally Untrained and Professionally Trained Subjects. Journal of Voice S089219972600189X. https://doi.org/10.1016/j.jvoice.2026.04.019
• Köberlein, M., Pilsl, T., Thiele, J., Gantner, I., Gellrich, D., Gantner, S., Kirsch, J., Echternach, M., 2026. Does Water Resistance Therapy Influence Vocal Vibrato In Classically Trained Singers? Journal of Voice. https://doi.org/10.1016/j.jvoice.2026.08.024

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License

This project is licensed under the GNU General Public License v3.0 or any later version (GPL-3.0-or-later).

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Contact

Jonas Kirsch — jonas.kirsch@med.uni-muenchen.de

Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, LMU University Hospital, LMU Munich, Germany

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