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105 results for “pitch”
Bag of pitch classes for Japanese enka songs
<p>This dataset is comprised of 827 songs, curated from the following publications:</p> <p>(a) Y. Goto (ed.), Grand Collection of Enka Songs by Female Singers 5th Ed. (in Japanese), Zen-on Music Co., 2016.<br> (b) Y. Goto (ed.), Grand Collection of Enka Songs by Male Singers 5th Ed. (in Japanese), Zen-on Music Co., 2016.</p> <p>Upon the use of this work, we kindly ask to explicitly cite/mention this paper, where the dataset was first used:</p> <p>[1] Eita Nakamura, Kentaro Shibata, Ryo Nishikimi, Kazuyoshi Yoshii, “Unsupervised melody style conversion,” Proc. 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 196-200, May 2019.</p> <p>** Data **</p> <p>EnkaDataBagOfPitchClasses.tsv</p> <p>Each line represents a song.</p> <p>The first column indicates the song's location in the publications. For example, EnkaF-030_p027 indicates the 30th song on page 27 by a female artist in (a).</p> <p>From the second column to the 13th column, the numbers represent the counts for pitch classes from C to B. Tied notes were merged and counted only once.</p>
Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics
<p>Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics.</p> <p>Including the simulation input parameter file and the necessary output data to plot each figure in the article. </p>
Jingju a cappella singing pitch contour segmentation ground truth dataset
<p>The dataset used in the paper:</p> <blockquote> <p>Gong, Rong; Yang, Yile; Serra, Xavier; Pitch Contour Segmentation for Computer-aided Jingju Singing Training Sound and Music Computing (SMC 2016), 2016, Hamburg, Germany</p> </blockquote> <p>is in "dataset" folder. The a cappella singing audio recordings are not contained in this folder due to their large size, please contact the paper authors to request them (rong.gong@upf.edu). In the "dataset" folder you can find:</p> <ol> <li>ground truth</li> <li>Jinging singing scores in .xml format used for estimating the bigram note transition probabilities.</li> </ol> <p>The ground truth annotation is used for:</p> <ul> <li>melodic transcription (male_12_pos_1 missing)</li> <li>parameter optimization,</li> <li>evaluating the StdCdLe thresholding and the overall segmentation performance.</li> </ul> <p>The subfolder "groundtruth" contains the following annotation for each jingju a cappella audio:</p> <ul> <li>file name: description (format)</li> <li>*_melodicTrans.csv: melodic transcription ground truth used for the evaluation (start_time pitch duration -).</li> <li>*_coarseSeg.csv: StdCdLe ground truth used for the parameter optimization and the evaluation (segmentation points).</li> <li>*_refinedSeg.csv: ground truth used for optimizing other parameters and the evaluation (start_time - duration).</li> <li>*_pitchtrack.csv: pitch track (contour) extracted by pYIN pitch-tracking algorithm (filename time pitch).</li> <li>*_monoNoteOut.csv: notes estimated by pYIN note-tracking algorithm (filename start_time duration pitch).</li> </ul> <p> </p> <p> </p>
Fig. 3 in Proboscis Monkeys (Nasalis Larvatus (Wurmb, 1787)) Have Unusually High-Pitched Vocalizations
Fig. 3. Cumulative curve of mean frequencies of calls in the study. Graph omits the one call that has a frequency <2.7 kHz.
Fig. 2. A in Proboscis Monkeys (Nasalis Larvatus (Wurmb, 1787)) Have Unusually High-Pitched Vocalizations
Fig. 2. A sample spectrogram of a high frequency vocalization of the proboscis monkey, Nasalis larvatus, that shows the harmonic structure of these calls. The fundamental frequency of this call ranges from 3.4–5.4 kHz and the mean frequency is 4.9 kHz.
Measuring embodied conceptualizations of pitch insinging performances: insights from an OpenPose study
<p>People conceptualize auditory pitch as vertical space: low and high pitch correspond to low and high space respectively. The strength of this cross-modal correspondence, however, seems to vary across different cultural contexts and a debate on the different factors underlying this variation is currently taking place. According to one hypothesis, pitch mappings are semantically mediated. For instance, the use of conventional metaphors such as ‘falling’ or ‘rising’ melodies strengthens a pitch-height mapping to the detriment of other possible mappings (e.g. pitch as bright/dark color or small/big size). Hence, entrenched pitch terms shape specific conceptualizations. The deterministic role of language is called into question by the hypothesis that different pitch mappings share a less constraining conceptual basis. As such, conceptual primitives may be concretized <em>ad hoc</em> into specific domains so that more local variation is possible<em>.</em> This claim is supported, for instance, by the finding that musicians use language-congruent (conventional) and language-incongruent (<em>ad hoc</em>) mappings interchangeably. The present paper substantiates this observation by investigating the head movements of musically trained and untrained speakers of Dutch in a melody reproduction task, as embodied instantiations of a vertical conceptualization of pitch. The OpenPose algorithm was used to track the movement trajectories in detail. The results show that untrained participants systematically made language-congruent movements, while trained participants showed more diverse behaviors, including language-incongruent movements. The difference between the two groups could not be attributed to the level of accuracy in the singing performances. In sum, this study argues for a joint consideration of more entrenched (e.g. linguistic metaphors) and more context-dependent (e.g. musical training and task) factors in accounting for variability in pitch representations.</p>
GWAS summary statistics and code for "Sequence variants affecting voice pitch in humans"
<p>Contents: GWAS summary statistics for voice pitch (median F0 in reading) and code for acoustic analysis</p> <p>Please refer to the corresponding publication:</p> <p>Gisladottir et al. Sequence variants affecting voice pitch in humans. <em>Science Advances</em></p> <p>The GWAS summary statistics is also available at: https://www.decode.com/summarydata/</p> <p>The code for acoustic analysis is also available at: https://github.com/cadia-lvl/deCODE</p> <p> </p> <p> </p> <p> </p>
The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty. in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA
The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty.
Vocal imitation of synthesised sounds varying in pitch, loudness and spectral centroid
<p>Dataset from the vocal imitation (production) task. Includes the audio stimuli, extracted audio features (for both stimuli and imitations) and extracted parameters, along with participant metadata. Please see the paper for further details. </p> <p>Details of fields in parameter_data.csv:</p> <p>Participant: index from 0-18</p> <p>sex: male/female</p> <p>singer: 1 if participant had been singer for > 5 years, 0 if not</p> <p>feature: feature that the parameter data was extracted for</p> <p>envelope: up/down for ramps, fast(5Hz)/slow(2Hz) for modulations</p> <p>fail: instances where the imitation failed to meet the criteria (see paper for details)</p> <p>rate: ratio of the modulation rate</p> <p>extent: extent of the modulation</p> <p>range: range of the ramp</p> <p>slope: slope of the ramp</p> <p>stimtype: type of stimulus, where single = single features, pitchamps = pitch & loudness combinations, pitchspecs = pitch & spectral centroid combinations</p> <p>stimlabel: label for each stimulus. The letter indicates the feature (p=pitch, a=loudness, s=spectral centroid) and the number indicates the envelope (1 = ramp down, 2 = ramp up, 3 = 5Hz modulation, 4 = 2Hz modulation)</p>
Duhumbi Phonology - Pitch and Tone
<p>Duhumbi is classified as a non-tonal language. Unlike several Central and East Bodish languages, in Duhumbi, distinctive tone cannot be conclusively attested even on the most common consonants affected by tone, namely the nasals /n, m, ng, ny/ and the approximants /l, r, w, y/. Nonetheless, Duhumbi clearly shows the first signs of the development of distinctive tone, or at least a distinctive pitch contrast. There are five main phonotactic conditions in which pitch distinctions can be observed, of which some form of glottalisation is the main one. The distinctive glottal constricted vowels /a, e, o, u/ in open syllables all have a rising pitch. The same holds for pre-glotallised initial vowels. Also, a high-falling pitch contour can be shown to be triggered by glottal reinforcement of syllable-final plosives. In addition, a high-falling versus low-level contour pitch contrast has been observed between lexemes with unaspirated unvoiced, aspirated unvoiced and unaspirated voiced plosive onsets. Finally, of the few attested contrastive minimal pairs for tone, the high onset/high-falling pitch lexemes of these minimal pairs can be shown to have Bodish cognates with a high onset, and the pitch contrast may thus be presumed to be borrowed as phonological feature of the entire lexeme.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
4 Bars Monophonic Melodies Dataset (Pitch Sequence)
<p>This dataset is designed for applications in music information retrieval, algorithmic composition, and machine learning tasks involving symbolic music data and it consists in a collection of unique 4 bars monophonic melodies represented as MIDI pitch sequences and each accompanied by thirteen attributes obtained with computational methods. The dataset has been generated using the <a href="https://github.com/resolv-libs" target="_blank" rel="noopener">Resolv</a> system's pipelines starting from the full version of the <a href="https://colinraffel.com/projects/lmd/">Lakh MIDI Dataset</a> (a collection of 176,581 unique MIDI files).</p> <p>The dataset has been used to train the models described in the papers:</p> <div> <blockquote> <p>M. Pettenò, A. I. Mezza, and A. Bernardini, "Conditional Diffusion As Latent Constraints for Controllable Symbolic Music Generation", in Proc. of the 26th International Society for Music Information Retrieval Conference (ISMIR 2025), Daejeon, Korea, S ept. 21-25, 2025.</p> <p>M. Pettenò, A. I. Mezza, and A. Bernardini, "On the Joint Minimization of Regularization Loss Functions in Deep Variational Bayesian Methods for Attribute-Controlled Symbolic Music Generation", in Proc. of the 33rd European Signal Processing Conference (EUSIPCO 2025), Palermo, Italy, Sept. 8-12, 2025.</p> </blockquote> </div> <p>The full article of this work also contains all the details on how the attributes have been obtained and on the implementation of the pipelines used for the generation and here it is worth to point out that melodies have been quanized to 4 steps per quarter and only 4/4 time signatures have been considered, hence each melody consists of N = 64 steps where each step is a number in the range [21-108], the MIDI pitches available in a standard piano, or a token in the set {128, 129} for hold note and note off events respectively. No additional performance features (e.g., dynamics, duration, or timing) are included, making this dataset a purely pitch-based collection.</p> <p>Three datasets (train, validation and test) are provided as <a href="https://www.tensorflow.org/tutorials/load_data/tfrecord" target="_blank" rel="noopener">TFRecord</a> file divided into 8 shards that contain the data in the Tensorflow's <a href="https://www.tensorflow.org/api_docs/python/tf/train/SequenceExample">SequenceExample</a> format in which the <em>feature_lists</em> field contains the pitch sequence as a list of integers and the <em>context</em> field its attributes.</p> <p>The table below shows the numbers of unique melodies contained in the three datasets.</p> <table> <tbody> <tr> <td> </td> <td><strong>Train</strong></td> <td><strong>Validation</strong></td> <td><strong>Test</strong></td> </tr> <tr> <td><strong>Total unique melodies</strong></td> <td>10,126,676</td> <td>70,908</td> <td>22,265</td> </tr> </tbody> </table> <p> </p> <p>And here is the list of computed attributes for each melody:</p> <table> <tbody> <tr> <td><strong>Attribute Name</strong></td> <td><strong><em>SequenceExample</em> Context Key</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Toussaint Metrical Complexity</td> <td><code>toussaint</code></td> <td>A metric that measures the degree of syncopation in rhythm patterns.</td> </tr> <tr> <td>Note Density</td> <td><code>note_density</code></td> <td>Measures the density of note onsets within the melody.</td> </tr> <tr> <td>Pitch Range</td> <td><code>pitch_range</code></td> <td>An indicator of how wide or narrow the melody is in terms of its pitch content.</td> </tr> <tr> <td>Contour</td> <td><code>contour</code></td> <td>Measures the degree to which the melody moves up or down.</td> </tr> <tr> <td>Note Change Ratio</td> <td><code>note_change_ratio</code></td> <td>The number of note changes normalized to the total number of steps N.</td> </tr> <tr> <td>Dynamic Range</td> <td><code>dynamic_range</code></td> <td>The difference between the maximum and minimum note velocities.</td> </tr> <tr> <td>Longest Repetitive Section</td> <td><code>len_longest_rep_section</code></td> <td>The length of the longest repetitive section in the melody normalized to the total number of steps N. A repetitive section is defined as a note that consecutively repeats at least r = 4 times.</td> </tr> <tr> <td>Repetitive Section Ratio</td> <td><code>repetitive_section_ratio</code></td> <td>The ratio between the total number of repetitive sections and a normalization factor N/r = 64/4 = 16.</td> </tr> <tr> <td>Hold Note Steps Ratio</td> <td><code>ratio_hold_note_steps</code></td> <td>The ratio between the number steps where a note is hold and the total steps N.</td> </tr> <tr> <td>Note Off Steps Ratio</td> <td><code>ratio_note_off_steps</code></td> <td>The ratio between the number steps where no note is played and the total steps N.</td> </tr> <tr> <td>Unique Notes Ratio</td> <td><code>unique_notes_ratio</code></td> <td>The ratio of unique notes is defined with respect to the total number of MIDI pitches considered (88) and the total number of steps N.</td> </tr> <tr> <td>Unique Bigrams Ratio</td> <td><code>unique_bigrams_ratio</code></td> <td>It is the ratio of the unique bigrams in the melody with respect to the total numbers of steps N.</td> </tr> <tr> <td>Unique Trigrams Ratio</td> <td><code>unique_trigrams_ratio</code></td> <td>It is the ratio of the unique trigrams in the melody with respect to the total numbers of steps N.</td> </tr> </tbody> </table> <p> </p> <p>To access the content of a <em>SequenceExample</em> use the <em>tf.io.parse_single_sequence_example</em>, for instance:<br><br></p> <div> <blockquote> <pre>tf.io.parse_single_sequence_example(<br> serialized_example,<br> context_features={<br> "toussaint": tf.io.FixedLenFeature([], dtype=tf.float32, default_value=0),<br> "note_density": tf.io.FixedLenFeature([], dtype=tf.float32, default_value=0),<br> },<br> sequence_features=["pitch_seq"]<br>)</pre> </blockquote> </div>
Investment Pitch and Quantifying Your Funding Needs_Video 3
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Investment Pitch and Quantifying Your Funding Needs_Video 2
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Investment Pitch and Quantifying Your Funding Needs_Video 1
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Pitch-angle resolved electron flux measurements in the Earth's radiation belt
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Nonlinear scattering of 90-degree pitch angle electrons in the outer radiation belt by large-amplitude EMIC waves
<p>This file is a collection of numeric data files which were used to create all figures in the paper (under review as of December 19, 2019). All the data were obtained from numerically solving the Lorentz equation of motion for 5 MeV electrons under EMIC waves superposed onto a dipolar background magnetic field. </p>
Fig. 1. A in Proboscis Monkeys (Nasalis Larvatus (Wurmb, 1787)) Have Unusually High-Pitched Vocalizations
Fig. 1. A male proboscis monkey in the Singapore Zoological Gardens.
Fig. S1 in Proboscis Monkeys (Nasalis Larvatus (Wurmb, 1787)) Have Unusually High-Pitched Vocalizations
Fig. S1. Spectrogram of Call 2: Low frequency vocalization produced by adult male.
Musical pitch interval comparisons in cochlear implants
<p>Music perception remains challenging for many cochlear implant (CI) recipients, due perhaps in part to the frequency mismatch that occurs between the electrode-neural interface and the frequencies allocated by the programming. Individual differences in ear anatomy, electrode array length, and surgical insertion can lead to great variability in the positions of electrodes within the cochlea, but these differences are not typically accounted for by current CI programming techniques. Flat panel computed tomography (FPCT) can be used to visualize the location of the electrodes and calculate the corresponding spiral ganglion characteristic frequencies. Such FPCT-based CI frequency mapping may improve pitch perception accuracy, and thus music appreciation, as well as speech perception. The present study seeks to develop a behavioral assessment metric for how well place-based pitch is represented across the frequency spectrum. Listeners were asked to match the pitch interval created by two tones, played sequentially, across different frequency ranges to estimate the extent to which pitch is evenly distributed across the CI array. This test was piloted with pure tones in normal hearing listeners, using both unprocessed and vocoder-processed sounds to simulate both matched and mismatched frequency-to-place maps. We hypothesized that the vocoded stimuli would be more difficult to match in terms of pitch intervals than unprocessed stimuli and that a warped map (as may occur with current clinical maps) would produce poorer matches than a veridical and even map (as may be achieved using FPCT-based frequency allocation). Preliminary results suggest that the task can reveal differences between veridical and warped maps in normal-hearing listeners under vocoded conditions. A small cohort of CI recipients performed similarly to a vocoded condition employing the same pitch map. The next steps will be to test this procedure in CI users and compare results with traditional clinical maps and FPCT-based frequency allocation to determine whether the FPCT-based maps result in improved pitch-interval perception.</p>
Supporting Information for "The Pitch Angle Measurement Method based on Detector Counts Distribution. -I. Basic conception"
<p><strong>Data </strong><strong>D</strong><strong>escription</strong></p> <p>We have uploaded 2 data files. These are:</p> <ol> <li>The CPD data of tn240719_200130. The data include: a) the relative time to reference time (UT 2024-07-19T20:01:30), b) the deposited energy (keV), c) the detector ID (i.e. CPD1 to CPD2). </li> <li>The GRD data of tn240719_200130. The data include: a) the relative time to reference time (UT 2024-07-19T20:01:30), b) the deposited energy (keV), c) the detector ID (i.e. GRD1 to GRD12). </li> </ol>
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