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openother-openMay 2022View details →

Data for the VoiceMOS Challenge 2022

This is the public release of the data for the first VoiceMOS Challenge.  The challenge had two tracks: a main track and an out-of-domain (OOD) track.  The data for the main track is known as the BVCC dataset, and contains samples from past Blizzard Challenges, Voice Conversion Challenges, and public samples from ESPnet-TTS, along with their mean opinion score (MOS) ratings collected in one unified listening test.  Standard training/development/testing splits from the challenge are also provided.  The OOD track contains samples from the Blizzard Challenge 2019 along with their ratings from the original, separate listening test.  We also include the scoring scripts that were used for the challenge. Samples from Blizzard Challenges may NOT be redistributed.  Blizzard samples are not included in this dataset, but the scripts to download and preprocess them are included.  Please run all of the included scripts to obtain the full dataset. Version 2 contains a new script, get_transcripts.py, and data file for gathering the transcripts of the audio samples from the Blizzard directories after those files have been downloaded using 01_get.py.  These are in the 'main' subdirectory. BVCC reference: Erica Cooper and Junichi Yamagishi, "How do Voices from Past Speech Synthesis Challenges Compare Today?" SSW 2021. https://arxiv.org/abs/2105.02373 The VoiceMOS Challenge: Wen-Chin Huang, Erica Cooper, Yu Tsao, Hsin-Min Wang, Tomoki Toda, Junichi Yamagishi, "The VoiceMOS Challenge 2022," submitted to Interspeech 2022.  https://arxiv.org/abs/2203.11389 https://voicemos-challenge-2022.github.io The Blizzard Challenges 2008, 2009, 2010, 2011, 2013, 2016, 2019: V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008. A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009. S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010. S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011. S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013. S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016. Z. Wu, Z. Xie, and S. King, "The Blizzard Challenge 2019," 2019. The Voice Conversion Challenges 2016, 2018, and 2020: T. Toda, L.-H. Chen, D. Saito, F. Villavicencio, M. Wester, Z. Wu, and J. Yamagishi, "The Voice Conversion Challenge 2016," Interspeech, 2016. J. Lorenzo-Trueba, J. Yamagishi, T. Toda, D. Saito, F. Villavicencio, T. Kinnunen, and Z. Ling, "The Voice Conversion Challenge 2018: Promoting development of parallel and nonparallel methods." Z. Yi, W.-C. Huang, X. Tian, J. Yamagishi, R. K. Das, T. Kinnunen, Z. Ling, and T. Toda, "Voice Conversion Challenge 2020 — intra-lingual semi-parallel and cross-lingual voice conversion —," in Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 2020, pp. 80–98. ESPnet-TTS: S. Watanabe, T. Hori, S. Karita, T. Hayashi, J. Nishitoba, Y. Unno, N. Enrique Yalta Soplin, J. Heymann, M. Wiesner, N. Chen, A. Renduchintala, and T. Ochiai, "ESPnet: End-to-end speech processing toolkit," in Proceedings of Interspeech, 2018, pp. 2207–2211. [Online]. Available: http://dx.doi.org/10.21437/ Interspeech.2018- 1456

Fig. 4 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Fig. 4. Megaselia sulphurizona Borgmeier, male terminalia, left lateral. Abbreviations: cl – cercus loop; hp- hypandrial process. Figs 5-9. Lateral habitus photographs of Megaselia species. Fig. 5. M. albizona new species. Fig. 6. M. sulphurizona Borgmeier. Fig. 7. M. nivizona new species. Fig. 8. M. reductizona new species. Fig. 9. M. solizona new species. Abbreviations: cl- cercus loop; hp- hypandrial process.

Fig. 3 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Fig. 3. Scatter graph of principle coordinates analysis of 11 transformed landmarks of two candidate species (identified by BOLD BIN) to be the "correct̎ Megaselia sulphurizona Borgmeier, plus those of the holotype.

Fig. 31 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Fig. 31. Scatter graph of principle coordinate ordination of wing landmarks of all species of the M. sulphurizona species complex. Species with more than three specimens in large font, species with 1 specimen in small font.

Fig. 34 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Fig. 34. Objective clustering diagram showing pairwise distance among species and geographical distributions.

Fig. 2 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Fig. 2. Scatter graph of principle coordinates analysis of 32 transformed landmarks of two candidate species (identified by BOLD BIN) to be the "correct̎ Megaselia sulphurizona Borgmeier.

Figs. 32-33 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Figs. 32-33. Details of Megaselia wing venation. 31. M. sulphurizona Borgmeier. 32. M. cryptizona new species. Abbreviations: ie – imaginary extension; sc – subcostal vein.

Figs. 15-22 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Figs. 15-22. Wings of Megaselia species. 15. M. albizona. 16. M. borealizona. 17. M. colombizona. 18. M. cryptizona. 19. M. danizona. 20. M. guanizona. 21. M. marizona. 22. M. nivizona.

Figs. 23-30 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Figs. 23-30. Wings of Megaselia species. 23. M. oklizona. 24. M. paulizona. 25. M. reductizona. 26. M. solizona. 27. M. sulphurizona. 28. M. tropizona. 29. M. wendizona. 30

Figs. 10-14 in Sixteen in One: White-Belted Megaselia Rondani (Diptera: Phoridae) From the New World Challenge Species Concepts

Figs. 10-14. Body parts of Megaselia species. Figs. 10-11.Venter of hypandrium. Fig. 10. M. albizona new species. Fig. 11. M. sulphurizona Borgmeier. Figs. 12-14. Dorsum of abdomen. Fig. 12. M. albizona new species. Fig. 13. M. reductizona new species. Fig. 14. M. sulphurizona Borgmeier. Abbreviation: hp- hypandrial process.

opencc-by-4.0Nov 2020View details →

Quantitative analysis techniques for evaluating the reliability of Li-ion battery challenges and solutions

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opencc-by-4.0Mar 2024View details →

Understanding the challenges and requirements for facilitating iStar learning: tool prototype presentation video

This is a presentation video about the tool prototype proposed by our work Understanding the challenges and requirements for facilitating iStar learning: a case study with iStar learners.

opencc-by-nc-sa-4.0Mar 2024View details →

DCASE 2024 Challenge Task 2 Development Dataset

<Data files will be made accessible on April 1st, 2024.> Description This dataset is the "development dataset" for the DCASE 2024 Challenge Task 2. The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel 10-second audio that includes both a machine's operating sound and environmental noise. The following seven types of real/toy machines are used in this task: <ul> <li>ToyCar</li> <li>ToyTrain</li> <li>Fan</li> <li>Gearbox</li> <li>Bearing</li> <li>Slide rail</li> <li>Valve</li> </ul> Overview of the task Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial-intelligence-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines.  This task is the follow-up from DCASE 2020 Task 2 to DCASE 2023 Task 2. The task this year is to develop an ASD system that meets the following five requirements. 1. **Train a model using only normal sound** (unsupervised learning scenario)   Because anomalies rarely occur and are highly diverse in real-world factories, it can be difficult to collect exhaustive patterns of anomalous sounds. Therefore, the system must detect unknown types of anomalous sounds that are not provided in the training data. This is the same requirement as in the previous tasks. 2. **Detect anomalies regardless of domain shifts** (domain generalization task)   In real-world cases, the operational states of a machine or the environmental noise can change to cause domain shifts. Domain-generalization techniques can be useful for handling domain shifts that occur frequently or are hard-to-notice. In this task, the system is required to use domain-generalization techniques for handling these domain shifts. This requirement is the same as in DCASE 2022 Task 2 and DCASE 2023 Task 2. 3. **Train a model for a completely new machine type**   For a completely new machine type, hyperparameters of the trained model cannot be tuned. Therefore, the system should have the ability to train models without additional hyperparameter tuning. This requirement is the same as in DCASE 2023 Task 2. 4. **Train a model using a limited number of machines from its machine type**   While sounds from multiple machines of the same machine type can be used to enhance the detection performance, it is often the case that only a  limited number of machines are available for a machine type. In such a case, the system should be able to train models using a few machines from a machine type. This requirement is the same as in DCASE 2023 Task 2. 5 . **Train a model both with or without attribute information** While additional attribute information can help enhance the detection performance, we cannot always obtain such information. Therefore, the system must work well both when attribute information is available and when it is not. The last requirement is newly introduced in DCASE 2024 Task2.   Definition We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.". <ul> <li>"Machine type" indicates the type of machine, which in the development dataset is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.</li> <li>A section is defined as a subset of the dataset for calculating performance metrics.</li> <li>The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.</li> <li>Attributes are parameters that define states of machines or types of noise. For several machine types, the attributes are hidden.</li> </ul>   Dataset This dataset consists of seven machine types. For each machine type, one section is provided, and the section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training, and (iii) 100 clips each of normal and anomalous sounds for the test. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.   File names and attribute csv files File names and attribute csv files provide reference labels for each clip. The given reference labels for each training/test clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Note that for machine types that has its attribute information hidden, the attribute information in each file names are only labeled as "noAttributes". Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format:     [filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]... For machine types that have their attribute information hidden, all columns except the filename column are left blank for each row. Recording procedure Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.   Directory structure - /dev_data       - /raw         - /fan             - /train (only normal clips)                   - /section_00_source_train_normal_0001_<attribute>.wav                   - ...                   - /section_00_source_train_normal_0990_<attribute>.wav                   - /section_00_target_train_normal_0001_<attribute>.wav                   - ...                   - /section_00_target_train_normal_0010_<attribute>.wav               - /test                  - /section_00_source_test_normal_0001_<attribute>.wav                     - ...                   - /section_00_source_test_normal_0050_<attribute>.wav                     - /section_00_source_test_anomaly_0001_<attribute>.wav                   - ...                   - /section_00_source_test_anomaly_0050_<attribute>.wav                   - /section_00_target_test_normal_0001_<attribute>.wav                 - ...                   - /section_00_target_test_normal_0050_<attribute>.wav                  - /section_00_target_test_anomaly_0001_<attribute>.wav                   - ...                   - /section_00_target_test_anomaly_0050_<attribute>.wav              - attributes_00.csv (attribute csv for section 00)     - /gearbox (The other machine types have the same directory structure as fan.)       - /bearing     - /slider (`slider` means "slide rail")     - /ToyCar       - /ToyTrain       - /valve     Baseline system The baseline system is available on the Github repository <<a href="https://github.com/nttcslab/dcase2023_task2_baseline_ae">https://github.com/nttcslab/dcase2023_task2_baseline_ae</a>>.The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.   Condition of use This dataset was created jointly by Hitachi, Ltd. and NTT Corporation and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.   Citation <TBD>   Contact If there is any problem, please contact us: <ul> <li>Tomoya Nishida, <a href="mailto:kota.dohi.gr@hitachi.com">tomoya.nishida.ax@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> <li>Noboru Harada, <a href="mailto:noboru@ieee.org">noboru@ieee.org</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>

opencc-by-4.0Apr 2024View details →

Supplemental package of a study on Microservices versus Monoliths: Identifying Challenges and Proposing Practical Recommendations

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opencc-by-4.0Apr 2024View details →

Dataset for the article 'Beyond Human Perception: Challenges in AI Interpretability of Orangutan Artwork'

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FIGURE 7 in Morphological and genetic data challenge species and subspecies in the Lerista microtis group (Squamata: Scincidae)

FIGURE 7. Longitudinal patterns of morphological variation in the L. microtis group. The x-axis represents a west–east geographical space across coastal southern Australia, and the y-axis describes important dimensions of morpho-spatial variation: (A) PC1 scores, derived from six mensural characters; (B) nMDS1 scores, and (C) nMDS2 scores, derived from five non-mensural characters. The line of best fit and 95% confidence interval are denoted by the black line and grey shaded zone, respectively. These graphs illustrate a mostly continuous and clinal structuring of phenotypic characters in the Lerista microtis group.

FIGURE 6 in Morphological and genetic data challenge species and subspecies in the Lerista microtis group (Squamata: Scincidae)

FIGURE 6. Biplot of non-metric multidimensional scaling (nMDS) performed on five qualitative characters (DP, ULSB, ULSW, MLSW, NSS) and one meristic (MSR; i.e., an integer count) character. These characters are among key traits Storr (1991a) used to diagnose these purported taxa. Specimens (coloured points) that are ordinated closer to one another are phenotypically more similar than those further apart. This graph lends support to the phenotypic distinctiveness of Lerista arenicola from L. microtis.

FIGURE 5 in Morphological and genetic data challenge species and subspecies in the Lerista microtis group (Squamata: Scincidae)

FIGURE 5. Biplot of the PCA performed on six mensural characters (SVL, HL, HW, AGD, Forelimb and Hindlimb). Axes show the first two principal components (i.e., PC1 and PC2) and their percentage of explained variation. Large, coloured ovals denote the 90% concentration ellipses for each taxonomic group proposed by Storr (1991a). Small points denote PC scores of individuals, whereas large points denote group centroids. Variables are denoted by arrows, the direction and length of which indicates their degree of contribution to each axis. Each variable is coloured according to its percentage contribution to its associated PC.

FIGURE 1 in Morphological and genetic data challenge species and subspecies in the Lerista microtis group (Squamata: Scincidae)

FIGURE 1. Distribution of currently recognized Lerista microtis subspecies and Lerista arenicola. Taxon assignment reflects that of the original museum records and the purported distribution as described in the literature (e.g., Storr 1991a; Wilson & Swan 2021). Note that all confirmed records of L. m. schwaneri are confined to islands near the SA mainland. Photographs of live specimens as follows: L. m. microtis (green)—Anders Zimny; L. m. intermedia (blue)—Jordan Vos; Lerista arenicola (red)— Brad Maryan; L. m. schwaneri (yellow)—Trevor Peters. Record colours are made slightly transparent to show overlapping records better, with more saturated colours indicating greater density of records.

FIGURE 3 in Morphological and genetic data challenge species and subspecies in the Lerista microtis group (Squamata: Scincidae)

FIGURE 3. Geographical distribution of the characters states proposed by Storr (1991a) to diagnose Lerista microtis subspecies and L. arenicola. There is geographic predictability for some phenotypes (e.g., width of mid-lateral and upper lateral stripe), but less so for others (e.g., dorsal patterning). Arrows denote specimens from inlands. White points in the SVL map are juvenile specimens, which were removed from morphological analyses.