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7 results for “audio features”
Audio features for DSL-S 2023 shared task
<p>These files are features (i-vectors, x-vectors, MFCC features) extracted from the subset of <a href="https://commonvoice.mozilla.org/">Mozilla Common Voice</a> corpus version 12.0 used in the <a href="https://sites.google.com/view/vardial-2023">VarDial 2023</a> shared task on <a href="https://dsl-s.github.io/">Discriminating Between Similar Languages - Speech</a> (DSL-S 2023).</p> <p>This data set contains only the features for the training and development section (and the Common Voice meta data) for the nine languages included in the shared task (see shared task website for further information).</p> <p> </p>
Hit Song Prediction (Million Song Dataset and Audio Features)
<p><strong>Hit Song Prediction Dataset</strong></p> <p>This dataset is based on the Million Song Dataset (MSD), which contains one million songs that are representative for western commercial music released between 1922 and 2011. The dataset contains release year information for 515,576 of the MSD songs. Please refer to http://millionsongdataset.com/ for further information on the million song dataset.</p> <p>For our hit song prediction experiments, we extract high- and low-level audio features using the Essentia toolkit (cf. https://essentia.upf.edu/). For the high-level features, we make use of the pre-trained classifiers as provided by Essentia. For a detailed description of the features, please visit the Essentia documentation.</p> <p><br> The dataset hence contains:</p> <ul> <li><strong>Audio features</strong>: the compressed msd_audio_features.tar.gz file contains the low- and high-level features for each track, stored as json files. Please note that we organize all MSD audio feature files based on the track's identifier with one folder holding all tracks with the same first letter of the track identifier to keep the files manageable. For each track, we provide two files: one containing the high-level and one containing the low-level features extracted by Essentia.</li> <li><strong>Billboard data:</strong> the folder billboard_data contains two files: msd_bb_matches.csv contains information about the MSD tracks that were also featured in the Billboard Hot 100 charts. Here, we provide the MSD id, Echo Nest id, artist name, track title, release year, peak position in Billboard charts and the number of weeks in the charts. The second file, msd_bb_non_matches.csv contains meta-information about the tracks of the MSD that were not featured in the Billboard Hot 100 and hence were used as negative samples. Here, we provide the MSD id, Echo Nest id, artist name, track title and the release year.</li> </ul> <p><br> If you make use of the dataset, please kindly cite the following paper:</p> <p>Eva Zangerle, Michael Vötter, Ramona Huber, and Yi-Hsuan Yang. Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019), 2019.</p> <p><br> @inproceedings{zangerle_ismir19,<br> title = {{Hit Song Prediction: Leveraging Low- and High-Level Audio Features}},<br> author = {Eva Zangerle and Ramona Huber and Michael V\"{o}tter and Yi-Hsuan Yang},<br> year = {2019},<br> booktitle = {{Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019)}},<br> }</p>
VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining
<p>The <em>VivesDebate-Speech</em> corpus contains the acoustic information of 29 different argumentative debates and the annotations of the segmentation (i.e., BIO tags) of the Argumentative Discourse Units identified in the spoken natural language discourse. </p>
Audio, visual, and verbal features from AVI video interviews in Koutsoumpis et al., 2024 (opva)
<p>The folder contains the audio, visual, and verbal features extracted from video data collected by Koutsoumpis et al., 2024 (where details on the data are provided). Details on data extraction can be found in Ghassemi et al., 2023. </p> <p>Files starting with "t1" refer to extracted audio, visual, and verbal features from Time 1. Files starting with "t2" refer to extracted audio, visual, and verbal features from Time 2, which took place 7 to 24 months after Time 1. In Time 2, a sub-sample of participants from Time 1 took the exact same study a second time.</p> <p> </p> <p>Ghassemi, S., Zhang, T., Van Breda, W.R.J., Holtrop, D.J., Oostrom, J.K., Koutsoumpis, A., & De Vries, R.E. (2023). Unsupervised multimodal learning for dependency-free personality recognition. IEEE Transactions on Affective Computing, 15(3), 1053-1066. https://doi.org/10.1109/TAFFC.2023.3318367</p> <p>Koutsoumpis, A., Ghassemi, S., Oostrom, J.K., Holtrop, D.J., Van Breda, W., Zhang, T. & De Vries, R.E. (2024). Beyond traditional job interviews: Psychometric analysis of asynchronous job video interviews for personality and interview performance evaluation using machine learning. Computers in Human Behavior, 154(108128). https://doi.org/10.1016/j.chb.2023.108128</p>
GCN-TFiLM: Modelling Black-box Audio Effects with Time-varying Feature Modulation
<p>Dataset, checkpoints, evaluation, and supplementary material associated with the following publication:</p> <p><a href="https://arxiv.org/abs/2211.00497">Modelling Black-box Audio Effects with Time-varying Feature Modulation</a></p> <p><strong>Citation:</strong></p> <pre><code>@inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} }</code></pre> <p> </p>
CLAP features for Audio Moment Retrieval
<p>This page includes CLAP features of three datasets used in <a href="https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/">Language-based audio moment retrieval</a> [1].</p> <ul> <li>Clotho-Moment</li> <li>UnAV100-subset</li> <li>TUT Sound Events 2017</li> </ul> <p>Raw wav files are also publicly available <a href="../records/13836117">here</a>.</p> <p>[1] H. Munakata, T. Nishimura, S. Nakada, T. Komatsu, "Language-based Audio Moment Retrieval", In Proc. ICASSP, 2024.</p> <h2>How to Use</h2> <p>We can train/evaluate audio moment retrieval models using these features in <a href="https://github.com/line/lighthouse">Lighthouse</a>.<br>Please check the instructions of Lighthouse.</p> <ol> <li>Unzip the file with the following commands<br>Clotho-moment: <br> <div> <div><code>for file in clotho-moment_features.tar.part-*.gz; do gunzip "$file"; done</code></div> <div><code>clotho-moment_features.tar.part-* > clotho-moment_features.tar</code></div> <div><code>tar -xvf clotho-moment_features.tar</code></div> </div> <br>UnAV100-subset, TUT Sound Events 2017: <br> <div> <div><code>tar -xvf tut2017_features.tar.gz</code></div> <div><code>tar -xvf unav100-subset_features.tar.gz</code></div> </div> </li> <li>Set symbolic links in Lighthouse<br><code>ln -s features/{dataset_name} {lighthouse_dir}/features<br></code></li> <li>Train the model<br><code>python training/train.py --model qd_detr --dataset clotho-moment --feature clap</code><br><br></li> <li>Evaluate the model<br> <div> <div><code>model=qd_detr</code><br> <div> <div><code>dataset=unav100-subset</code></div> <div><code>feature=clap</code></div> <div><code>model_path={lighthouse_dir}/results/qd_detr/clotho-moment/clap/best.ckpt</code></div> <div><code>eval_split_name=val</code></div> <div><code>eval_path=data/unav100-subset/unav100-subset_test_release.jsonl</code></div> <br><code>python training/evaluate.py \</code> <div><code>--model $model \</code></div> <div><code>--dataset $dataset \</code></div> <div><code>--feature $feature \</code></div> <div><code>--model_path $model_path \</code></div> <div><code>--eval_split_name $eval_split_name \</code></div> <div><code>--eval_path $eval_path</code></div> </div> </div> </div> <br><br><br><br></li> </ol>
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