Package index • superassp

Pitch & F0 Tracking

Fundamental frequency estimation — 18 algorithms spanning C++ (fastest), classical signal processing, and deep learning.

trk_pitch_rapt(): Track fundamental frequency using RAPT (Robust Algorithm for Pitch Tracking)
trk_pitch_swipe(): Track fundamental frequency using SWIPE (Sawtooth Waveform Inspired Pitch Estimator)
trk_pitch_dio(): DIO Pitch Tracking (C++ implementation)
trk_pitch_harvest(): Harvest Pitch Tracking (C++ implementation)
trk_pitch_reaper(): Track fundamental frequency using REAPER (Robust Epoch And Pitch EstimatoR)
trk_pitch_yin(): Track fundamental frequency using the YIN algorithm
trk_pitch_pyin(): Track fundamental frequency using probabilistic YIN (pYIN)
trk_pitch_pda(): Track fundamental frequency using the ESTk PDA algorithm
trk_tandem(): Track pitch and voiced speech using the TANDEM-STRAIGHT algorithm
trk_pitch_swiftf0(): Track fundamental frequency using SwiftF0 (ONNX)
trk_pitch_crepe(): Track fundamental frequency and periodicity using CREPE (ONNX)
trk_pitch_cc(): Pitch tracking via Praat's cross-correlation method
trk_pitch_ac(): Pitch tracking via Praat's autocorrelation method
trk_pitch_shs(): Pitch tracking via Praat's subharmonic summation (SHS) method
trk_pitch_spinet(): Pitch tracking via Praat's SPINET method
trk_pitch_srh(): Track fundamental frequency using the Summation of Residual Harmonics (SRH)
trk_pitch_ksv(): Track fundamental frequency using the KSV periodicity detector
trk_pitch_mhs(): Track pitch using the Modified Harmonic Sieve algorithm
trk_pitch_snack(): Track fundamental frequency using the Snack/ESPS dp_f0 algorithm

Formant Analysis

Formant frequency and bandwidth tracking.

trk_formant_forest(): Track formant frequencies and bandwidths (FOREST)
trk_formant_deepformants(): Track formant frequencies using DeepFormants (ONNX)
trk_formant_tvwlp(): Track formants using Time-Varying Weighted Linear Prediction (TVWLP)
trk_formant_burg(): Formant frequencies and bandwidths via Praat's Burg method
trk_formant_cgdzp(): Track formants using Chirp Group Delay Zero-Phase (CGDZP) analysis
trk_formant_snack(): Track formants and bandwidths using the Snack/ESPS LPC tracker
trk_formant_formantnet(): Track formant frequencies and bandwidths using FormantNet (ONNX)

Spectral Analysis

Spectrum estimation, cepstral analysis, and spectral moments.

trk_dft_spectrum(): Track short-term DFT power spectrum
trk_css_spectrum(): Track cepstrally-smoothed spectrum
trk_lps_spectrum(): Track LP-smoothed spectrum
trk_cepstrum(): Track short-term cepstral coefficients
trk_spectral_moments(): Spectral moments (CoG, SD, skewness, kurtosis)
trk_mfcc(): Extract Mel-Frequency Cepstral Coefficients (MFCCs) via SPTK
trk_cheap_trick(): CheapTrick Spectral Envelope Estimation (WORLD vocoder, C++ implementation)

Energy & Amplitude

Signal energy, zero-crossings, autocorrelation, and intensity.

trk_rms(): Track short-term RMS amplitude
trk_zcr(): Track short-term zero-crossing rate
trk_acf(): Track short-term autocorrelation function
trk_intensity(): Sound intensity contour

Voice Quality & Aperiodicity

Comprehensive voice quality assessment — from single-track measures to 132-parameter dysphonia toolboxes.

trk_d4c(): Estimate band aperiodicity using the D4C algorithm (WORLD vocoder)
trk_cpps(): Cepstral Peak Prominence Smoothed (CPPS)
trk_vuv(): Voiced/unvoiced segmentation via two-pass adaptive pitch detection
trk_praatsauce(): Comprehensive voice quality feature set via PraatSauce
lst_covarep_vq(): Extract voice quality parameters (NAQ, QOQ, H1-H2, HRF, PSP) per utterance
lst_vq(): Voice Quality Measurements using pladdrr
lst_pharyngeal(): Pharyngeal Voice Quality Analysis
lst_voice_report(): Voice Report Analysis (pladdrr)
lst_voice_tremor(): Vocal Tremor Analysis Using pladdrr
lst_dsi(): Dysphonia Severity Index (DSI) Analysis (pladdrr)
lst_avqi(): Acoustic Voice Quality Index (AVQI) using pladdrr
trk_covarep_creak(): Detect creaky voice (vocal fry) per frame
trk_covarep_env_te(): Estimate spectral envelope using the True Envelope (Teager energy) method
trk_covarep_vad_drugman(): Detect voiced frames using Drugman's multi-branch VAD
trk_covarep_vq_gci(): Track GCI-anchored voice quality measures as a time series
trk_creak_vat(): Detect creaky voice using the Kane-Drugman VAT creak detector
trk_gci_vat(): Detect glottal closure instants (GCIs) using SE-VQ via voiceanalysis
trk_iaif_vat(): Estimate glottal flow via IAIF using voiceanalysis
trk_mdq_vat(): Track Maxima Dispersion Quotient (MDQ) for breathy/tense voice discrimination
trk_peakslope_vat(): Peak slope via voiceanalysis Daless wavelet bank
trk_peakslope(): Track spectral tilt using D'Alessandro PeakSlope (Morlet wavelet)
trk_pitch_vat(): Track fundamental frequency using SRH via the voiceanalysis package
trk_hmpd(): Extract harmonic model phase distortion features (HMPD): AE, PDM, PDD
lst_lf_vat_synthesis(): Synthesise an LF model glottal pulse via voiceanalysis
lst_vq_vat(): Per-GCI voice-quality summary via voiceanalysis
lst_polarity(): Signal polarity detection (RESKEW algorithm)

Prosody & Intonation

Prosodic features, rhythm, articulation complexity, and pitch modelling.

lst_dysprosody(): Extract Dysprosody Prosodic Features
lst_voxit(): Extract Voxit prosodic complexity features from audio files
lst_vowel_space(): Vowel space analysis (F1×F2 area ratio)
momel(): Run MOMEL algorithm on F0 values
intsint(): Run INTSINT algorithm on MOMEL targets
prosody_measures(): Compute prosodic measures from audio file or Sound object
voxit-analysis-stats: Voxit analysis statistics
voxit-dsp-utils: DSP utility functions from Voxit

Source-Filter Decomposition

Glottal source and vocal tract separation.

trk_gfmiaif(): Decompose speech into vocal tract, glottis, and lip radiation LP filters (GFM-IAIF)
trk_covarep_iaif(): Extract glottal flow waveform using Iterative Adaptive Inverse Filtering (IAIF)

OpenSMILE Feature Sets

Standardized acoustic feature extraction via OpenSMILE C++.

lst_GeMAPS(): Compute the GeMAPS openSMILE feature set (C++ Implementation)
lst_eGeMAPS(): Compute the eGeMAPS openSMILE feature set
lst_emobase(): Compute the emobase openSMILE feature set
lst_ComParE_2016(): Compute the ComParE 2016 openSMILE feature set

Epoch Detection

Glottal closure instants and pitch marks.

trk_pitchmark_estk(): Detect glottal closure instants in laryngograph signals using ESTk pitchmark
trk_pitchmark_reaper(): Detect glottal closure instants using REAPER (pitch marks)
lst_covarep_gci_sedreams(): SEDREAMS Glottal Closure Instant Detection

Psychoacoustics

Equal-loudness contours and loudness unit conversions (ISO 226, ISO 532).

iso226_phon: ISO 226:2023 Phon (Loudness Level) Conversions
iso532-sone: ISO 532 Sone (Loudness) Conversions

Unit Conversion

Psychoacoustic scale conversions (Hz, Bark, ERB, Mel, semitone, phon, sone).

ucnv_bark_to_hz(): Convert Bark Scale to Frequency
ucnv_db_and_hz_to_phon(): Convert Sound Pressure Level and Frequency to Loudness Level (Phon)
ucnv_db_and_hz_to_sone(): Convert dB and Hz Directly to Sone
ucnv_erb_to_hz(): Convert ERB-rate Scale to Frequency
ucnv_hz_to_bark(): Convert Frequency to Bark Scale
ucnv_hz_to_erb(): Convert Frequency to ERB-rate Scale
ucnv_hz_to_mel(): Convert Frequency to Mel Scale
ucnv_hz_to_semitone(): Convert Frequency to Semitones
ucnv_mel_to_hz(): Convert Mel Scale to Frequency
ucnv_phon_and_hz_to_db(): Convert Loudness Level (Phon) and Frequency to Sound Pressure Level
ucnv_phon_to_sone(): Convert Phon to Sone
ucnv_semitone_to_hz(): Convert Semitones to Frequency
ucnv_sone_and_hz_to_db(): Convert Sone and Hz to dB
ucnv_sone_to_phon(): Convert Sone to Phon

I/O — Audio & SSFF

Load audio, read/write SSFF signal files.

read_audio(): Read an audio file into an AsspDataObj
av_to_asspDataObj(): Convert audio file to AsspDataObj
avaudio_to_av(): Convert AVAudio to av::read_audio_bin Format
avaudio_to_tempfile(): Convert AVAudio to Temporary WAV File
read_ssff(): Read an SSFF or audio file into an AsspDataObj
write_ssff(): Write an AsspDataObj to an SSFF file
prep_recode(): Re-encode Media File with Custom Parameters

I/O — JSON Track Format (JSTF)

Create, read, write, and manipulate JSON Track Format files.

read_track(): Unified Track Reading Interface
write_track(): Write Track to File
create_json_track_obj(): Create a JsonTrackObj
append_json_track_slice(): Append a slice to JsonTrackObj
merge_json_tracks(): Merge multiple JsonTrackObj files
subset_json_track(): Subset JsonTrackObj
validate_json_track(): Validate JsonTrackObj
store_slice(): Provides the ability to store a multidimensional feature set related to a part of a signal.
json_track_core: JSON Track Object Core Functions
json_track_methods: JSON Track Conversion Methods
read_jstf(): Read JSTF File
write_jstf(): Write JSTF Object to File
jstf_io: JSTF (JSON Sparse Track Format) I/O Functions

Classes & Data Structures

Core data classes, S7 generics, and AsspDataObj accessors.

is_avaudio(): Check if Object is AVAudio
as_avaudio(): Convert to AVAudio Object
as.data.frame(<AsspDataObj>) print(<AsspDataObj>) as_tibble(<AsspDataObj>) cut(<AsspDataObj>): AsspDataObj — ASSP Data Object
print(<JsonTrackObj>) as.data.frame(<JsonTrackObj>) as_tibble(<JsonTrackObj>) summary(<JsonTrackObj>): JsonTrackObj — JSON Track Format Object
s7-methods: S7 Method System for DSP Functions
sample_rate() n_records() signal_duration() start_time() track_names() file_path() track_formats() dur() numRecs() rate() startTime() tracks(): Accessor methods for AsspDataObj and JsonTrackObj

ASSP Constants & Types

Enumerated types and constants from the ASSP signal processing library.

AsspFileFormats: list of possibly useful file formats for AsspDataObj corresponding to the first element of the fileInfo attribute
AsspLpTypes(): AsspLpTypes
AsspSpectTypes(): AsspSpectTypes
AsspWindowTypes(): AsspWindowTypes
isAsspLpType(): isAsspLpType
isAsspSpectType(): isAsspSpectType
isAsspWindowType(): isAsspWindowType
wrasspOutputInfos: list of default output extensions, track names and output type for each signal processing function in wrassp

Package Utilities

Introspection, visualisation, and media conversion helpers.

get_track_label(): Get track label for plotting
get_track_label_expr(): Get track label as expression for plotting
ggtrack(): Create ggplot with automatic track labels
differentiate(): Derivation of SSFF track objects

Python / pladdrr Integration Helpers

Audio loading and format conversion helpers for pladdrr backends.

av_load_for_pladdrr(): Load audio file as pladdrr Sound object
pladdrr_df_to_superassp(): Convert pladdrr data frame to superassp format
rmsana_memory(): Perform RMS analysis on AsspDataObj in memory
process_media_file(): Process audio from any media file format

Legacy Functions

Pre-v1.0 functions retained for backward compatibility. Prefer the trk_* equivalents for new code.

trk_pitch_mhs(): Track pitch using the Modified Harmonic Sieve algorithm
harmonics(): Compute the harmonic frequency structure from f0 measurements
trk_afdiff(): Differentiate an audio waveform
trk_affilter(): Apply a digital filter to audio signals
trk_arf(): Track LP-derived vocal tract area function coefficients
trk_lar(): Track LP-derived log area ratios
trk_lpc(): Track LP filter coefficients
trk_rfc(): Track LP reflection coefficients
useWrasspLogger: package variable to force the usage of the logger set to FALSE by default
read.AsspDataObj(): read.AsspDataObj from a signal/parameter file
write.AsspDataObj(): write.AsspDataObj to file