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10 results for “vocal imitation”
Evidence for individual vocal recognition in a pair-bonding poison frog, Ranitomeya imitator
<p>Individually distinctive vocalizations are widespread in nature, although the ability of receivers to discriminate these signals has only been explored through limited taxonomic and social lenses. Here, we asked whether anuran advertisement calls, typically studied for their role in territory defense and mate attraction, facilitate recognition and preferential association with partners in a pair-bonding poison frog (<em>Ranitomeya imitator</em>). Combining no- and two-stimulus choice playback experiments, we evaluated behavioral responses of females to male acoustic stimuli. Virgin females oriented to and approached speakers broadcasting male calls independent of caller identity, implying that females are generally attracted to male acoustic stimuli outside the context of a pair bond. When pair-bonded females were presented with calls of a mate and a stranger, they showed significant preference for calls of their mate. Moreover, behavioral responses varied with breeding status: females with eggs were faster to approach stimuli than females that were pair-bonded but did not currently have eggs. Our study suggests a potential role for individual vocal recognition in the formation and maintenance of pair bonds in a poison frog and raises new questions about how acoustic signals are perceived in the context of monogamy and biparental care.</p>
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>
Evidence for individual vocal recognition in a pair-bonding poison frog, Ranitomeya imitator
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Vocal imitation of percussion sounds: on the perceptual similarity between imitations and imitated sounds
<p>Dataset of the drum sounds and vocal imitations used in the listening study. There are 30 drum sounds, indexed 0-29. The imitations are indexed by imitator (0-13), with imitations of each drum sound in the respective directories. Included is a csv file containing the participant responses from the listening test.</p> <p>NOTE: The BFD drum samples have been made available with the permission of FXpansion Audio UK. Permission is granted for their use in further academic research. Contact SKoT McDonald <skot@fxpansion.com> for further information."</p> <p> </p> <p> </p>
Vocal Imitation Set v1.1.3 : Thousands of vocal imitations of hundreds of sounds from the AudioSet ontology
<p>The VocalImitationSet is a collection of crowd-sourced vocal imitations of a large set of diverse sounds collected from Freesound (<a href="https://freesound.org/">https://freesound.org/</a>), which were curated based on Google's AudioSet ontology (<a href="https://research.google.com/audioset/">https://research.google.com/audioset/</a>). We expect that this dataset will help research communities obtain a better understanding of human's vocal imitation and build a machine understand the imitations as humans do.</p> <p>See <a href="https://github.com/interactiveaudiolab/VocalImitationSet">https://github.com/interactiveaudiolab/VocalImitationSet</a> for more information about this dataset and its latest updates.</p> <p>For citations, please use this reference:</p> <p>Bongjun Kim, Madhav Ghei, Bryan Pardo, and Zhiyao Duan, "Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology," <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)</em>, Nov. 2018.</p> <p>Contact Info:</p> <p>- Interactive Audio Lab: <a href="http://music.eecs.northwestern.edu/">http://music.eecs.northwestern.edu</a></p> <p>- Bongjun Kim <a href="mailto:bongjun@u.northwestern.edu">bongjun@u.northwestern.edu</a> | <a href="http://www.bongjunkim.com/">http://www.bongjunkim.com</a></p> <p>- Bryan Pardo <a href="mailto:pardo@northwestern.edu">pardo@northwestern.edu</a> | <a href="http://www.bryanpardo.com/">http://www.bryanpardo.com</a></p>
Vocal imitations of non-vocal sounds
<p>Vocal imitations of everyday sounds. There are two families (interactions and products), 10 imitators (I#), 8 categories, and 2 sounds per categories</p>
ESC-50-Voice: Dataset of vocal imitation for environmental sound in ESC-50
<h2><strong>Description</strong></h2> <p>This is a dataset with vocal imitation, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice for an environmental sound in ESC-50 [1] that can be used in various tasks that use environmental sounds. The dataset consists of 9,920 vocal imitations (8 imitators per environmental sound). Each imitator is a Japanese speaker. All audio data are 48kHz/16bit wav files. </p> <p>Each audio file is named as follows:</p> <pre><code>vocal_imitation/SpeakerID/FileName_SpeakerID.wav</code></pre> <p>FileName means the original audio file name in ESC-50. SpaekerID means the ID of the imitator. We recorded vocal imitations for a part of sound events in ESC-50. A list of the sound events used can be obtained from EventList.csv.</p> <p>Note that this dataset does not contain environmental sound files, which can be obtained from ESC-50. Environmental sounds in ESC-50 are available <a href="https://github.com/karolpiczak/ESC-50">here</a>.</p> <h2><strong>Terms of use</strong></h2> <p>The materials may be used free of charge for research purposes, but please refrain from redistribution or use that is offensive to public order and morals. If you want to use for commercial purposes, please contact us (Yuki Okamoto or Keisuke Imoto).</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset, please cite as follow:</p> <p>Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi, Ryotaro Nagase, Takahiro Fukumori, and Yoichi Yamashita, "Environmental Sound Synthesis from Vocal Imitations and Sound Event Labels," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 411-415, 2024.</p> <h2><strong>Feedback</strong></h2> <p>If there is any problem, please contact us</p> <ul> <li>Yuki Okamoto, <a href="mailto:y-okamoto@ieee.org">y-okamoto@ieee.org</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul> <p> </p> <p>[1] K. J. Piczak, "Esc: Dataset for environmental sound classification,” in Proc. the 23rd ACM International Conference on Multimedia, 2015, p. 1015–1018.</p>
Fine-grained Vocal Imitation Set
<p>This dataset includes 763 vocal imitations of 108 sound events. The sound event recordings were taken from a subset of Vocal Imitation Set (<a href="http://zenodo.org/record/1340763">zenodo.org/record/1340763</a>). While the original VocalImitationSet only contains vocal imitations of a single reference recording per class, this new dataset contains vocal imitations of multiple reference recordings per class. Class names and filenames in this dataset are matched with the VocalImitationSet. Read the following paper to get more detailed information about VocalImitationSet.</p> <p>[<a href="https://interactiveaudiolab.github.io/assets/papers/DCASE2018_Kim.pdf">pdf</a>] Bongjun Kim, Madhav Ghei, Bryan Pardo, and Zhiyao Duan, "Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology," *Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)*, Nov. 2018.</p> <p>Contact Info:</p> <p>- Interactive Audio Lab: <a href="http://music.eecs.northwestern.edu/">http://music.eecs.northwestern.edu</a></p> <p>- Bongjun Kim <a href="mailto:bongjun@u.northwestern.edu">bongjun@u.northwestern.edu</a> | <a href="http://www.bongjunkim.com/">http://www.bongjunkim.com</a></p> <p>- Bryan Pardo <a href="mailto:pardo@northwestern.edu">pardo@northwestern.edu</a> | <a href="http://www.bryanpardo.com/">http://www.bryanpardo.com</a></p>
In vivo assessment of the neural substrate linked with vocal imitation accuracy
Human speech and bird song are acoustically complex communication signals that are learned by imitation during a sensitive period early in life. Although the brain areas indispensable for speech and song learning are known, the neural circuits important for enhanced or reduced vocal performance remain unclear. By combining in vivo structural Magnetic Resonance Imaging with song analyses in juvenile male zebra finches during song learning and beyond, we reveal that song imitation accuracy correlates with the structural architecture of four distinct brain areas, none of which pertain to the song control system. Furthermore, the structural properties of a secondary auditory area in the left hemisphere, are capable to predict future song copying accuracy, already at the earliest stages of learning, before initiating vocal practicing. These findings appoint novel brain regions important for song learning outcome and inform that ultimate performance in part depends on fac tors experienced before vocal practicing.
In vivo assessment of the neural substrate linked with vocal imitation accuracy
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