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10 results for “synthetic mixture”
[DCASE2022 Task 3] Synthetic SELD mixtures for baseline training
<p><strong>DESCRIPTION:</strong><br> <br> This audio dataset serves serves as supplementary material for the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">DCASE2022 Challenge Task 3: Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes</a>. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the <a href="https://zenodo.org/record/5476980">TAU-NIGENS Spatial Sound Scenes 2021</a> dataset for the <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection-results">DCASE2021 Challenge Task 3</a>. </p> <p>The SELD task setup in DCASE2022 is based on spatial recordings of real scenes, captured in the <a href="https://zenodo.org/record/6387880">STARS22</a> dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the <a href="https://github.com/sharathadavanne/seld-dcase2022">baseline model</a>, and they are shared for reasons of reproducibility. For more details on the task setup, please refer to the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">task description</a>.</p> <p>Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see:</p> <ul> <li>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</li> </ul> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li><strong>13 target sound classes</strong> (see task description for details)</li> <li>The sound event samples are sources from the <strong><a href="https://zenodo.org/record/4060432">FSD50K</a></strong> dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here.</li> <li><strong>1200</strong> 1-minute long spatial recordings</li> <li>Sampling rate of<strong> 24kHz</strong></li> <li>Two 4-channel recording formats, first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array (<strong>MIC</strong>)</li> <li>Spatial events spatialized in <strong>9 unique rooms</strong>, using measured RIRs for the two formats</li> <li>Maximum <strong>polyphony of 2</strong> (with possible same-class events overlapping)</li> <li>Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a <strong>separation into a training and testing split</strong>, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only).</li> <li>The training split is indicated as <strong>fold1</strong> in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K.</li> <li>The testing split is indicated as <strong>fold2</strong> in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K.</li> <li>Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup.</li> </ul> <p><strong>FSD50K SELECTION:</strong></p> <p>The list of selected sound event recordings is included along the recordings and metadata, as <strong>FSD50K_selected.txt</strong>. Each line in the text file has the following structure:</p> <pre><code>[target_label]/[train/test]/[FSD50K_label]/filename.wav</code></pre> <p>with an example:</p> <pre><code>domesticSounds/train/Boiling/16584.wav</code></pre> <p>meaning that the file 16584.wav from FSD50K, with the <em>Boiling</em> label of FSD50K, is included in the samples for the training split of those synthetic recordings, and it is mapped to the target class of <em>domestic sounds. </em>Note that there can be multiple FSD50K labels mapped the same target class. Also note that if these are downloaded from FSD50K, and a folder structure is created that replicates the structure in the list, the resulting folder can be used out-of-the-box with the scene generator to generate new mixtures with the same or different parameters.</p> <p>Note that no sounds form FSD50K have been selected for the <em>Music</em> target class. Background and pop music tracks from the public domain have been cropped and used instead.</p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Synthetic Escherichia coli mixture samples with variable coverage
<p>This dataset contains the synthetic mixture samples and reference sequences - as well as the appropriate metadata - that were originally used in the 2021 revision of the mSWEEP manuscript.<br> <br> There are 87 samples in total, each containing 100bp paired-end Illumina sequencing reads from 10 different <em>Escherichia coli </em>strains from 10 different lineages. The number of reads is set so that the sequencing coverage of the individual strains varies between 50x and 0.10x and sums up to 100x.</p>
[DCASE2024 Task 3] Synthetic SELD mixtures for baseline training
<p><strong>DESCRIPTION:</strong><br><br>This audio dataset serves serves as supplementary material for the <a href="https://dcase.community/challenge2024/task-audio-and-audiovisual-sound-event-localization-and-detection-with-source-distance-estimation">DCASE2024 Challenge Task 3: Audio and Audiovisual Sound Event Localization and Detection with Distance Estimation</a>. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the <a href="../record/5476980">TAU-NIGENS Spatial Sound Scenes 2021</a> dataset for the <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection-results">DCASE2021 Challenge Task 3</a>. </p> <p>The SELD task setup in DCASE2024 is based on spatial recordings of real scenes, captured in the <a href="../records/7880637">STARS23</a> dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the <a href="https://github.com/partha2409/DCASE2024_seld_baseline">baseline model</a>. For more details on the task setup, please refer to the <a href="https://dcase.community/challenge2024/task-audio-and-audiovisual-sound-event-localization-and-detection-with-source-distance-estimation">task description</a>.</p> <p>Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see:</p> <ul> <li>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</li> </ul> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li><strong>13 target sound classes</strong> (see task description for details)</li> <li>The sound event samples are sources from the <strong><a href="../record/4060432">FSD50K</a></strong> dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here.</li> <li><strong>1200</strong> 1-minute long spatial recordings</li> <li>Sampling rate of<strong> 24kHz</strong></li> <li>Two 4-channel recording formats, first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array (<strong>MIC</strong>)</li> <li>Spatial events spatialized in <strong>9 unique rooms</strong>, using measured RIRs for the two formats</li> <li>Maximum <strong>polyphony of 3</strong> (with possible same-class events overlapping)</li> <li>Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a <strong>separation into a training and testing split</strong>, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only).</li> <li>The training split is indicated as <strong>fold1</strong> in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K.</li> <li>The testing split is indicated as <strong>fold2</strong> in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K.</li> <li>Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup.</li> </ul> <p> </p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Data from: A synthetic biology and green bioprocess approach to recreate agarwood sesquiterpenoid mixtures
<p>Certain endangered Thymelaeaceous trees are major sources of the fragrant and highly valued resinous agarwood, comprised of hundreds of oxygenated sesquiterpenoids (STPs). Despite growing pressure on natural agarwood sources, the chemical complexity of STPs severely limits synthetic production. Here, we catalogued the chemical diversity in 58 agarwood samples by two-dimensional gas chromatography–mass spectrometry and partially recreated complex STP mixtures through synthetic biology. We improved STP yields in the unicellular alga <em>Chlamydomonas reinhardtii </em>by combinatorial engineering to biosynthesise nine macrocyclic STP backbones found in agarwood. A bioprocess following green-chemistry principles was developed that exploits 'milking' of STPs without cell lysis, solvent–solvent STP extraction, solvent–STP nanofiltration, and bulk STP oxy-functionalisation to obtain terpene mixtures like those of agarwood. This process occurs with total solvent recycling and enables continuous production. Our synthetic-biology approach offers a sustainable alternative to harvesting agarwood trees to obtain mixtures of complex, fragrant, oxygenated STPs.</p>
Data from: A synthetic biology and green bioprocess approach to recreate agarwood sesquiterpenoid mixtures
Open the record for dataset details and reuse information.
Figure 4 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 4 Representative chromatograms obtained from A. The blank and B. Spiked plasma samples. Legend: Peak numbers: DXM-2, STG-3, RDV-4 and GLM-5.
Figure 3 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 3 Representative chromatograms obtained from A. The mobile phase and B. Synthetic mixture. Legend: Peak numbers: MTF-1, DXM-2, STG-3, RDV-4, and GLM-5.
Toxicogenomic responses in rainbow trout hepatocytes exposed to model chemicals and a synthetic mixture
GEO Series GSE6416. Salmo salar; Coregonus clupeaformis; Oncorhynchus mykiss; Oncorhynchus tshawytscha; Osmerus mordax. 40 samples. Type: Expression profiling by array.
Figure 1 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 1 Chemical structures of RDV, DXM, MTF, STG and GLM.
Figure 2 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 2 UV spectra of RDV, DXM, MTF, STG and GLM.
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