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6 results for “fundamental frequency”
Exploring the relation between fundamental frequency and spectral envelope in the perception of musical instrument sounds – sound files and participant responses
<p>This database contains synthesized instrument sounds (sounds.zip), participant responses (data.zip), and a key for the stimulus order (stimKey.zip) as complementary data to [1].</p> <p>Sounds include individual stimuli for two experiments. Experiment 1 contains stimuli used for sound pleasantness and sound brightness ratings. Every rating scale includes three acoustic conditions: congruent, incongruent, and fixed, corresponding to the relation of fundamental frequency (F0) and spectral envelope (SE). See [1] for further details on this matter. Experiment 2 contains stimuli for four synthesized instrument sounds: violin, alto voice, clarinet, and tuba. Sounds were synthesized using congruent spectral envelopes (all), and register fixed spectral envelopes (low, mid, high).</p> <p>All sounds are mono signals with a sampling frequency of 44100 Hz in WAV format.</p> <p>Data includes four sets of participant response data: sound pleasantness ratings (Exp. 1), sound brightness ratings (Exp. 1), sound pleasantness ratings (Exp. 2), and sound plausibility ratings (Exp. 2).</p> <p>StimKey provides values for the first and second principal components of the synthesis space for the pleasantness (1 to 81) and brightness (1 to 16) stimulus numbers for Exp. 1.</p> <p> </p> <p>Names convention for Exp. 2 sounds:</p> <p>InstrumentName_congruencyCondition_F0..wav</p> <p> </p> <p>References:</p> <p>[1] Jacobsen, S. and Siedenburg, K. (2024). Exploring the relation between fundamental frequency and spectral envelope in the perception of musical instrument sounds. Acta Acustica, 8, 48. <a href="https://doi.org/10.1051/aacus/2024038">https://doi.org/10.1051/aacus/2024038</a>.</p>
Convergence in voice fundamental frequency in a joint speech production task - Dataset
<p>This dataset contains fundamental frequency values for 30 pairs of participants performing an alternate reading task.</p> <p>Fundamental frequency in each speaker's speech was artificially modified in real-time during the task. We provide both the untransformed and transformed fundamental frequency values.</p> <p>Full description of the experimental setup is found in < insert paper DOI here ></p> <p>The data is organised as follows:</p> <ul> <li>data for each pair is stored in a separate folder with the pair ID as the folder name</li> <li>each folder contains two repetitions of the task, as produced in a zero-phase and pi-phase condition, respectively</li> <li>file names ending with '<strong>f0</strong>' contain fundamental frequency data, sampled every 10 ms</li> <li>file names ending with '<strong>turns</strong>' contain time onsets of speaking turns</li> </ul> <p>The format of '<strong>f0</strong>' files is as follows:</p> <ul> <li>'<strong>t</strong>': time in seconds</li> <li>'<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>)</li> <li>'<strong>f0_unstransf</strong>': fundamental frequency values as produced by the participant (untransformed) in Hertz</li> <li>'<strong>f0_transf</strong>': fundamental frequency values as heard by the other participant (transformed) in Hertz</li> </ul> <p>The format of '<strong>turns</strong>' files is as follows:</p> <ul> <li>'<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>), or both participants at once (<em>joint</em>)</li> <li>'<strong>turn</strong>': index of the reading turn</li> <li>'<strong>type</strong>': turn type. Either <em>speech</em> or <em>silence</em> for each participant, or <em>turn</em> for the joint description.</li> <li>'<strong>t</strong>': turn onset in seconds</li> </ul> <p> </p>
A Replication Dataset for Fundamental Frequency Estimation
<p><em>Part of the dissertation <a href="http://localhost:8000/index.html">Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods</a>.<br> © 2020, Bastian Bechtold. All rights reserved.</em></p> <p> </p> <p>Estimating the fundamental frequency of speech remains an active area of research, with varied applications in speech recognition, speaker identification, and speech compression. A vast number of algorithms for estimatimating this quantity have been proposed over the years, and a number of speech and noise corpora have been developed for evaluating their performance. The present dataset contains estimated fundamental frequency tracks of 25 algorithms, six speech corpora, two noise corpora, at nine signal-to-noise ratios between -20 and 20 dB SNR, as well as an additional evaluation of synthetic harmonic tone complexes in white noise.</p> <p>The dataset also contains pre-calculated performance measures both novel and traditional, in reference to each speech corpus’ ground truth, the algorithms’ own clean-speech estimate, and our own consensus truth. It can thus serve as the basis for a comparison study, or to replicate existing studies from a larger dataset, or as a reference for developing new fundamental frequency estimation algorithms. All source code and data is available to download, and entirely reproducible, albeit requiring about one year of processor-time.</p> <p>Included Code and Data</p> <ul> <li><code>ground truth data.zip</code> is a <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> dataset of fundamental frequency estimates and ground truths of all speech files in the following corpora: <ul> <li><a href="http://www.festvox.org/cmu_arctic/">CMU-ARCTIC</a> (<em>consensus truth</em>) [1]</li> <li><a href="http://www.cstr.ed.ac.uk/research/projects/fda/">FDA</a> (<em>corpus truth</em> and <em>consensus truth</em>) [2]</li> <li><a href="https://lost-contact.mit.edu/afs/nada.kth.se/dept/tmh/corpora/KeelePitchDB/">KEELE</a> (<em>corpus truth</em> and <em>consensus truth</em>) [3]</li> <li><a href="http://www.cstr.ed.ac.uk/research/projects/artic/mocha.html">MOCHA-TIMIT</a> (<em>consensus truth</em>) [4]</li> <li><a href="https://www.spsc.tugraz.at/databases-and-tools/ptdb-tug-pitch-tracking-database-from-graz-university-of-technology.html">PTDB-TUG</a> (<em>corpus truth</em> and <em>consensus truth</em>) [5]</li> <li><a href="https://catalog.ldc.upenn.edu/LDC93S1">TIMIT</a> (<em>consensus truth</em>) [6]</li> </ul> </li> <li><code>noisy speech data.zip</code> is a <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> datasets of fundamental frequency estimates of speech files mixed with noise from the following corpora: <ul> <li><a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX</a> [7]</li> <li><a href="https://research.qut.edu.au/saivt/databases/qut-noise-databases-and-protocols/">QUT-NOISE</a> [8]</li> </ul> </li> <li><code>synthetic speech data.zip</code> is a <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> dataset of fundamental frequency estimates of synthetic harmonic tone complexes in white noise.</li> <li><code>noisy_speech.pkl</code> and <code>synthetic_speech.pkl</code> are pickled <a href="https://pandas.pydata.org/">Pandas</a> dataframes of performance metrics derived from the above data for the following list of fundamental frequency estimation algorithms: <ul> <li>AUTOC [9]</li> <li>AMDF [10]</li> <li><a href="http://www2.ece.rochester.edu/projects/wcng/code.html">BANA</a> [11]</li> <li>CEP [12]</li> <li><a href="https://github.com/marl/crepe">CREPE</a> [13]</li> <li><a href="http://www.kki.yamanashi.ac.jp/~mmorise/world/english/">DIO</a> [14]</li> <li><a href="http://web.cse.ohio-state.edu/pnl/software.html">DNN</a> [15]</li> <li><a href="https://github.com/LvHang/pitch">KALDI</a> [16]</li> <li>MAPS</li> <li><a href="http://www.seas.ucla.edu/spapl/shareware.html">MBSC</a> [17]</li> <li><a href="https://github.com/jkjaer/fastF0Nls">NLS</a> [18]</li> <li><a href="http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html">PEFAC</a> [19]</li> <li><a href="https://github.com/praat/praat">PRAAT</a> [20]</li> <li><a href="http://www.speech.kth.se/wavesurfer/links.html">RAPT</a> [21]</li> <li><a href="http://labrosa.ee.columbia.edu/projects/SAcC/">SACC</a> [22]</li> <li><a href="http://www.seas.ucla.edu/spapl/weichu/safe/">SAFE</a> [23]</li> <li><a href="https://mathworks.com/matlabcentral/fileexchange/1230">SHR</a> [24]</li> <li>SIFT [25]</li> <li><a href="https://github.com/covarep/covarep">SRH</a> [26]</li> <li><a href="https://github.com/HidekiKawahara/legacy_straight">STRAIGHT</a> [27]</li> <li><a href="http://www.cise.ufl.edu/~acamacho/english/curriculum.html">SWIPE</a> [28]</li> <li><a href="http://www.ws.binghamton.edu/zahorian/yaapt.htm">YAAPT</a> [29]</li> <li><a href="http://audition.ens.fr/adc/">YIN</a> [30]</li> </ul> </li> <li><code>noisy speech evaluation.py</code> and <code>synthetic speech evaluation.py</code> are Python programs to calculate the above <a href="https://pandas.pydata.org/">Pandas</a> dataframes from the above <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> datasets. They calculate the following performance measures: <ul> <li>Gross Pitch Error (GPE), the percentage of pitches where the estimated pitch deviates from the true pitch by more than 20%.</li> <li>Fine Pitch Error (FPE), the mean error of grossly correct estimates.</li> <li>High/Low Octave Pitch Error (OPE), the percentage pitches that are GPEs and happens to be at an integer multiple of the true pitch.</li> <li>Gross Remaining Error (GRE), the percentage of pitches that are GPEs but not OPEs.</li> <li>Fine Remaining Bias (FRB), the median error of GREs.</li> <li>True Positive Rate (TPR), the percentage of true positive voicing estimates.</li> <li>False Positive Rate (FPR), the percentage of false positive voicing estimates.</li> <li>False Negative Rate (FNR), the percentage of false negative voicing estimates.</li> <li>F₁, the harmonic mean of precision and recall of the voicing decision.</li> </ul> </li> <li><code>Pipfile</code> is a pipenv-compatible pipfile for installing all prerequisites necessary for running the above Python programs.</li> </ul> <p>The Python programs take about an hour to compute on a fast 2019 computer, and require at least 32 Gb of memory.</p> <p>References:</p> <ol> <li>John Kominek and Alan W Black. CMU ARCTIC database for speech synthesis, 2003.</li> <li>Paul C Bagshaw, Steven Hiller, and Mervyn A Jack. Enhanced Pitch Tracking and the Processing of F0 Contours for Computer Aided Intonation Teaching. In EUROSPEECH, 1993.</li> <li>F Plante, Georg F Meyer, and William A Ainsworth. A Pitch Extraction Reference Database. In Fourth European Conference on Speech Communication and Technology, pages 837–840, Madrid, Spain, 1995.</li> <li>Alan Wrench. MOCHA MultiCHannel Articulatory database: English, November 1999.</li> <li>Gregor Pirker, Michael Wohlmayr, Stefan Petrik, and Franz Pernkopf. A Pitch Tracking Corpus with Evaluation on Multipitch Tracking Scenario. page 4, 2011.</li> <li>John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, and Victor Zue. TIMIT Acoustic-Phonetic Continuous Speech Corpus, 1993.</li> <li>Andrew Varga and Herman J.M. Steeneken. Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recog- nition systems. Speech Communication, 12(3):247–251, July 1993.</li> <li>David B. Dean, Sridha Sridharan, Robert J. Vogt, and Michael W. Mason. The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms. Proceedings of Interspeech 2010, 2010.</li> <li>Man Mohan Sondhi. New methods of pitch extraction. Audio and Electroacoustics, IEEE Transactions on, 16(2):262—266, 1968.</li> <li>Myron J. Ross, Harry L. Shaffer, Asaf Cohen, Richard Freudberg, and Harold J. Manley. Average magnitude difference function pitch extractor. Acoustics, Speech and Signal Processing, IEEE Transactions on, 22(5):353—362, 1974.</li> <li>Na Yang, He Ba, Weiyang Cai, Ilker Demirkol, and Wendi Heinzelman. BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):1833–1848, December 2014.</li> <li>Michael Noll. Cepstrum Pitch Determination. The Journal of the Acoustical Society of America, 41(2):293–309, 1967.</li> <li>Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. CREPE: A Convolutional Representation for Pitch Estimation. arXiv:1802.06182 [cs, eess, stat], February 2018. arXiv: 1802.06182.</li> <li>Masanori Morise, Fumiya Yokomori, and Kenji Ozawa. WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications. IEICE Transactions on Information and Systems, E99.D(7):1877–1884, 2016.</li> <li>Kun Han and DeLiang Wang. Neural Network Based Pitch Tracking in Very Noisy Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):2158–2168, Decem- ber 2014.</li> <li>Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur. A pitch extraction algorithm tuned for automatic speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 2494–2498. IEEE, 2014.</li> <li>Lee Ngee Tan and Abeer Alwan. Multi-band summary correlogram-based pitch detection for noisy speech. Speech Communication, 55(7-8):841–856, September 2013.</li> <li>Jesper Kjær Nielsen, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, and Søren Holdt Jensen. Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient. Signal Processing, 135:188–197, June 2017.</li> <li>Sira Gonzalez and Mike Brookes. PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(2):518—530, February 2014.</li> <li>Paul Boersma. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In Proceedings of the institute of phonetic sciences, volume 17, page 97—110. Amsterdam, 1993.</li> <li>David Talkin. A robust algorithm for pitch tracking (RAPT). Speech coding and synthesis, 495:518, 1995.</li> <li>Byung Suk Lee and Daniel PW Ellis. Noise robust pitch tracking by subband autocorrelation classification. In Interspeech, pages 707–710, 2012.</li> <li>Wei Chu and Abeer Alwan. SAFE: a statistical algorithm for F0 estimation for both clean and noisy speech. In INTERSPEECH, pages 2590–2593, 2010.</li> <li>Xuejing Sun. Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, volume 1, page I—333. IEEE, 2002.</li> <li>Markel. The SIFT algorithm for fundamental frequency estimation. IEEE Transactions on Audio and Electroacoustics, 20(5):367—377, December 1972.</li> <li>Thomas Drugman and Abeer Alwan. Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics. In Interspeech, page 1973—1976, 2011.</li> <li>Hideki Kawahara, Masanori Morise, Toru Takahashi, Ryuichi Nisimura, Toshio Irino, and Hideki Banno. TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation. In Acous- tics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, pages 3933–3936. IEEE, 2008.</li> <li>Arturo Camacho. SWIPE: A sawtooth waveform inspired pitch estimator for speech and music. PhD thesis, University of Florida, 2007.</li> <li>Kavita Kasi and Stephen A. Zahorian. Yet Another Algorithm for Pitch Tracking. In IEEE International Conference on Acoustics Speech and Signal Processing, pages I–361–I–364, Orlando, FL, USA, May 2002. IEEE.</li> <li>Alain de Cheveigné and Hideki Kawahara. YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4):1917, 2002.</li> </ol>
Studying Newborns' Brain Activity in the NICU Through a Musical Intervention: the Role of Fundamental Frequency
ClinicalTrials.gov study NCT06398912. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Sexual selection on male vocal fundamental frequency in humans and other anthropoids
In many primates, including humans, the vocalizations of males and females differ dramatically, with male vocalizations and vocal anatomy often seeming to exaggerate apparent body size. These traits may be favoured by sexual selection because low-frequency male vocalizations intimidate rivals and/or attract females, but this hypothesis has not been systematically tested across primates, nor is it clear why competitors and potential mates should attend to vocalization frequencies. Here we show across anthropoids that sexual dimorphism in fundamental frequency (F0) increased during evolutionary transitions towards polygyny, and decreased during transitions towards monogamy. Surprisingly, humans exhibit greater F0 sexual dimorphism than any other ape. We also show that low-F0 vocalizations predict perceptions of men's dominance and attractiveness, and predict hormone profiles (low cortisol and high testosterone) related to immune function. These results suggest that low male F0 signals condition to competitors and mates, and evolved in male anthropoids in response to the intensity of mating competition.
Data from: Sexual selection on male vocal fundamental frequency in humans and other anthropoids
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