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1,300 results for “Sounds”
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation for Domain Generalization Task
<p><strong>Description</strong></p> <p>This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task (MIMII DG). The dataset consists of normal and abnormal operating sounds of five different types of industrial machines, i.e., fans, gearboxes, bearing, slide rails, and valves. The data for each machine type includes three subsets called "sections", and each section roughly corresponds to a type of domain shift. <strong>This dataset is a subset of the dataset for <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2022 Challenge Task 2</a>, so the dataset is entirely the same as data included in the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a>. </strong>For more information, please see the pages of the <a href="https://zenodo.org/record/6355122#.Ynt7rtrP2Uk">development dataset</a> and the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">task description</a><strong> </strong>for DCASE 2022 Challenge Task 2.</p> <p> </p> <p><strong>Baseline system</strong></p> <p>Two simple baseline systems are available on the Github repositories <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_ae">autoencoder-based baseline</a> and <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_mobile_net_v2">MobileNetV2-based baseline</a>. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was made by <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>We will publish a paper on the dataset and will announce the citation information for them, so please make sure to cite them if you use this dataset.</p> <p> </p> <p><strong>Feedback</strong></p> <p>If there is any problem, pease contact us</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
Data from: Crowdsourcing training material for automated bird sound classification – a pilot study
<p>Data from the manuscript "Crowdsourcing training material for automated bird sound classification – a pilot study" by Petteri Lehikoinen, Meeri Rannisto, Ulisses Camargo, Aki Aintila, Patrik Lauha, Esko Piirainen, Panu Somervuo & Otso Ovaskainen</p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
<p><span><span>An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. <br></span></span></p> <p><span><span>In this article we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps.<br></span></span></p> <p><span><span>Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification.<br></span></span></p> <p><span><span>Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.</span></span></p>
Data for "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure"
<p>This file is the dataset used in the article "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure", Nat. Commun. 13, 7211 (2022). https://doi.org/10.1038/s41467-022-34789-2</p>
Bangru Language Data - Cut sound files
<p>These files form the empirical basis for the following article:</p> <p>Bodt, Timotheus Adrianus and Ismael Lieberherr. 2015. First notes on the phonology and classification of the Bangru language of India. <em>Linguistics of the Tibeto-Burman Area 38:1</em> (2015), 66–123.</p> <p>doi 10.1075/ltba.38.1.03bod</p> <p>issn 0731–3500 / e-issn 2214–5907 © John Benjamins Publishing Company</p> <p>These data were collected in Sarli circle, Kurung Kumey district, Arunachal Pradesh, India.</p> <p>The data collectors were the following faculty, students and associated researchers of the Department of English and Foreign Languages, Tezpur University, Assam, India:</p> <p>Nupur Sinha (Faculty), Ismael Lieberherr (Affiliated PhD scholar), Timotheus A. Bodt (Affiliated PhD scholar), Diksha Konwar, Eshani Baishya, Nawaf Helmi, Pinaz Mirza, Ratul Mahela, Sansuma Brahma (students).</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>
Figure 5. Low-pass filter-EKG Through Sound-Card
<p>The low-pass filter, presented in figure 5, allows signals with frequencies up to 34 Hz (cutoff<br> frequency) to pass unaltered while it strongly attenuates those with frequencies exceeding the<br> cut-off frequency. It is an active filter, of the butterworth sallen-key type which contains one of the<br> four operational amplifiers of the TL084 integrated circuit.</p>
Figure 18. Adjusting the signal input through the microphone socket-EKG Through Sound-Card
<p>After that, the patient will be asked to wait for a few minutes, during which he will relax,<br> and we will adjust the amplitude of the input signal into the computer’s sound-card, through the<br> microphone socket. For this, we will use the program Sound Control from Start → Programs →<br> Accessories → Entertaiment → Sound Control (Figure 18).</p>
Figure 2. Patient protection in relation to the parasite signals of electric lines-EKG Through Sound-Card
<p>The recorder module applies the signal to the preamplifier’s non-inverted input and then<br> passes through the limiter which establishes the maximum left or right limits of the stylus, to avoid<br> breaking the recording tape. The power supply of the device contains mainly a convertor with an<br> output transformer and a reaction transformer powered either from a network through a downward<br> transformer followed by a rectifier and filter, or from an accumulator battery.<br> To comply with the rules of patient protection, the supply for the electrocardiograph’s<br> preamplifier is done floatingly according to the grounding null (Figure 2).<br> Figure 2.</p>
Figure 1. Bipolar limb derivations-EKG Through Sound-Card
<p>Since the human body is a conductive mass, an electrode attached to the arm is the electric<br> equivalent of a connection to the shoulder and an electrode attached to the foot is the equivalent of a<br> connection to the abdomen. Using this principle, we obtain the following three standard bipolar<br> limb derivations (Figure1):<br> • Derivation I: negative electrode to the right hand and positive electrode to the left hand<br> • Derivation II: positive electrode to the left foot, negative to the right hand<br> • Derivation III: positive electrode to the left foot, negative to the left hand.</p>
Multi-domain evaluation of a latest generation combustion engine: focusing on sound quality perception
<p>Dataset for conference paper "Multi-domain evaluation of a latest generation combustion engine: focusing on sound quality perception"</p>
Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle
<p>Dataset for conference paper "Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle"</p>
Room Impulse Responses for Low-Frequency Sound Field Control
<h2>About</h2> <div> <div> <div> <p>A dataset of room impulse responses (RIRs) measured in the low frequency range, for different measurement signal lengths, in two rooms with different acoustic conditions. Acquired with the purpose of low-frequency sound zones rendering and evaluation, the dataset can be used in general for different sound field control methods.</p> <p>By design, the dataset is composed of two sets of RIRs: one intended for the design of the control strategies, and another one intended for evaluation [1]. The RIRs of the first set, obtained with measurement signals of different length, allow exploring the influence of the acquisition time of the RIRs in the control methods [2]. The RIRs of the second set allow evaluating the sound field generated at and around the position of the RIRs of the first set.</p> <p>The RIRs were acquired with the Synchronized Swept-Sine (SSS) method, proposed by Novak et al. [3], at a samplig frequency of 48 kHz with SSS signals varying from 15 Hz to 600 Hz. The obtained RIRs were re-sampled to 1.2 kHz.</p> <p>Two files with different contents have been added:</p> <ul> <li><strong>RIR_LF_SFC_Light:</strong> contains the documentation, MatLab codes, and the ready-to-use RIRs stored as 3D-arrays in .mat files.<br><br></li> <li><strong>RIR_LF_SFC_Full:</strong> in addition to files in <strong>RIR_LF_SFC_Light</strong>, it contains the original SSS signals and the signals recorded during the measurements. These were used to retrieve the RIRs and therefore, can be used for custom purposes. <br><br></li> </ul> </div> </div> </div> <div> <h2>Citation</h2> <p>Please cite the database with the following paper:</p> <p>@inproceedings{cadavid_ATvsSS_2024,<br>title={Spatial Sampling versus Acquisition Time of Room Impulse<br> Responses for Low-Frequency Sound Zones},<br> author={Cadavid, Jos{\'e} and M{\o}ller, Martin Bo and van Waterschoot, Toon and Bech, S{\o}ren and {\O}stergaard, Jan},<br> booktitle={Audio Engineering Society Convention 156},<br> year={2024},<br> organization={Audio Engineering Society} }</p> <h2>Acknowledgements</h2> <p>The authors would like to thank <a href="https://orcid.org/0000-0002-1452-2227" target="_blank" rel="noopener">Antonin Novak</a>, <a href="https://orcid.org/0000-0002-2175-6603" target="_blank" rel="noopener">Christian S. Pedersen</a>, and Claus Vestergaard for their help with the RIRs measurements.</p> <p>This project has received funding from the European Union’s (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions Grant No. 956369.</p> </div>
Dataset: Sound Group Inc. (SOGP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sound Financial Bancorp, Inc. (SFBC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DATASET Marine sounds below 2 kHz from French Polynesia
<div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This file contains sounds (mainly from fish but not exclusively) from wave files subsampled at 4 kHz and recorded in French Polynesia. The original files are accessible on Zenodo (10.5281/zenodo.11960304). A first version of a dichotomous identification key was published in the supplementary material of Raick et al. 2023a (10.1007/s00338-022-02343-7), while a more complete version is available on Zenodo (10.5281/zenodo.10592328). The related scientific publications are Raick et al. 2023a, Raick et al. 2023b, and Raick et al. 2024.</p> </div> </div> </div> </div> </div> </div>
Sound-VECaps
<p>This is the dataset for Sound-VECaps, a large-scale audio dataset with visual-enhanced captions. </p> <p>We also release the dataset for AudioCaps-Enhanced, the visual-enhanced AudioCaps testing dataset as the new benchmark. </p>
Fig. 10. Males possess a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 10. Males possess a well-developed file on the lef elytron a), with a relatively underdeveloped file on the right elytron b). The development of a file in female D. approximatus was highly variable and ofen almost non-existent. Lef elytron of female with a well-developed file, relative to most other observed female files c), and of female with poorly-developed file d).
Fig. 7. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 7. Waveform a) and spectrogram b) of simple chirps produced by a male (first and third chirps) and female (second and fourth chirps) when paired together in gallery. The spectral profile c) was taken at the center time of the first chirp, highlighted in a and b. Center time is the point during a sample about which energy is divided equally.
Fig. 8. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 8. Waveform a) and spectrogram b) of overlapping chirps produced by female and male D. approximatus. The first chirp in the sequence was produced by the female and is repeated approximately every 0.5 s. The female chirp overlaps with male chirps between 2 and 5 s in the recording. Sound occurring just past 4 s was not produced by either beetle, but rather was accidental noise produced by the recorder.
Fig. 9 in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 9. Abdominal segments of male a) and female b) D. approximatus. The male plectrum is shown circled in a).
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Allen Brain Atlas
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OpenNeuro
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