Paper deep dive
TSExplorer: An interactive data annotation and exploration tool for time-series data
Einari Vaaras, Manu Airaksinen, Okko Räsänen
Intelligence
Status: not_run | Model: - | Prompt: - | Confidence: 0%
Entities (0)
Relation Signals (0)
No relation signals yet.
Cypher Suggestions (0)
No Cypher suggestions yet.
Abstract
Abstract:We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-dimensional datasets through multiple complementary 2D visualizations derived from high-dimensional feature representations. TSExplorer is designed as a general-purpose research tool supporting a wide range of workflows, including exploratory data analysis, annotation of unlabeled or partially-labeled datasets, comparison of feature representations, and post-hoc inspection and refinement of existing labels with interactive visual feedback.
Tags
Links
- Source: https://arxiv.org/abs/2608.30514v1
- Canonical: https://arxiv.org/abs/2608.30514v1
Trouble viewing inline? Open PDF directly →
Full Text
11,374 characters extracted from source content.
Expand or collapse full text
TSExplorer: An interactive data annotation and exploration tool for time-series data Einari Vaaras ID 1,∗ , Manu Airaksinen ID 2 , Okko R ̈ as ̈ anen ID 1 1 Signal Processing Research Centre, Tampere University, Finland 2 BABA Center, Department of Physiology, University of Helsinki, Finland einari.vaaras@tuni.fi, manu.airaksinen@helsinki.fi, okko.rasanen@tuni.fi Abstract We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool en- ables users to inspect high-dimensional datasets through mul- tiple complementary 2D visualizations derived from high- dimensional feature representations. TSExplorer is designed as a general-purpose research tool supporting a wide range of workflows, including exploratory data analysis, annotation of unlabeled or partially-labeled datasets, comparison of feature representations, and post-hoc inspection and refinement of ex- isting labels with interactive visual feedback. Index Terms: 2D visualization, data annotation, human- computer interaction, data exploration, human-in-the-loop 1. Introduction Many modern analysis pipelines rely on high-dimensional fea- ture representations when working with time-series data, such as speech, video, physiological signals like electroencephalog- raphy, sensor-based recordings, or multimodal combinations of these modalities. These representations may be hand-crafted (e.g. log-mel spectrograms), or they can also be learned embed- dings (e.g. wav2vec 2.0 [1]). While such representations can be powerful, common workflows for data annotation and analysis either discard them entirely or make limited use of their struc- ture. In practice, data annotation is frequently performed in a sequential manner, and data analysis is typically based on static visualizations or aggregate summary statistics such as feature variances, class-wise averages, performance metrics, or corre- lation measures. Such static approaches make it difficult to ex- plore individual samples in detail or to understand their rela- tionships within the overall feature space. This limitation can be particularly noticeable for large-scale datasets, or when com- paring how the dataset is structured with different feature rep- resentations. To address these challenges, we present Time-Series Ex- plorer (TSExplorer), a general-purpose graphical user inter- face (GUI) tool for time-series data annotation and exploration. TSExplorer allows users to freely explore high-dimensional datasets through interactive 2D visualizations (2DVs). Instead of following a single pre-defined workflow, TSExplorer is de- signed as a flexible tool that can be used for data annotation, analysis, and interpretation. Interactive 2D data visualization has previously been shown to support human-in-the-loop ex- ploratory data analysis and interpretation (e.g. [2, 3]), as well as data annotation (e.g. [4, 5]). TSExplorer is publicly available on GitHub 1 , and it has been previously described and evaluated in Vaaras et al. [6]. ** indicates the corresponding author. 1 https://github.com/SPEECHCOG/TSExplorer Figure 1: A screenshot of TSExplorer with the RAVDESS dataset [7]. This example contains a video widget (top left), a spectrogram widget (bottom left), and a scatter widget (right). 2. Interactive Data Annotation and Exploration with TSExplorer The TSExplorer GUI (Figure 1) visualizes the entire dataset as a 2D scatter plot, where each data point corresponds to one data sample (e.g. an utterance in speech data). Selecting a point in the scatter plot presents corresponding views (e.g. au- dio, video, and signal waveforms) of the sample to the user. Users can alternate between t-distributed stochastic neighbor embedding (t-SNE) [8], principal component analysis (PCA), and uniform manifold approximation and projection (UMAP) [9] 2DVs, each highlighting different aspects of the underlying high-dimensional data. Furthermore, users can switch between different high-dimensional feature representations, both within and across modalities, from which the 2DVs are computed. In TSExplorer, users can select samples for inspection or annotation either by left-clicking data points in the scatter plot, or algorithmically using the next sample button. Currently, there are three variants for algorithmic sample selection avail- able: 1) Random, where samples are selected at random; 2) Or- dered, where samples are selected sequentially from the dataset; 3) Farthest-first, which selects samples using the farthest-first traversal algorithm [10]. Samples can also be enqueued for later inspection via right-clicking, after which they can be either se- quentially browsed using the next sample button, or by directly left-clicking them in the scatter plot. The scatter plot supports zooming using the scroll wheel of the mouse, and the data point arXiv:2608.30514v1 [cs.HC] 31 Aug 2026 Speech (audio) Video Physiological signals Sensor data Etc. Raw time-series data Time-frequency representations (e.g. spectrograms) Learned embeddings (e.g. wav2vec 2.0) Statistical and distributional descriptors (e.g. mean, autocorrelation, percentiles) Etc. Feature extraction t-SNE PCA UMAP 2DV computation Data annotation Outlier identification Exploratory analysis Label refinement Etc. TSExplorer OfflineOnline Figure 2: A block diagram of the TSExplorer workflow. First, high-dimensional features are computed offline from time-series data. Then, 2DVs are computed from these features, either pre-computed (offline) or within TSExplorer (online). Finally, the time-series data, high-dimensional features, and 2DVs are provided as inputs to TSExplorer for interactive data annotation and exploration. sizes adapt dynamically. Labels can be modified at any given time either using a drop-down menu or with keyboard shortcuts. Sample colors in the scatter plot reflect their labels (green color for unlabeled samples by default), and the currently selected sample is highlighted with a large ring (yellow by default). TSExplorer supports unlabeled, partially-labeled, and fully-labeled datasets. For unlabeled data, the tool can be used for data annotation and exploratory analysis, e.g. to help un- derstand dataset structure or identify outliers. For partially- labeled data, TSExplorer supports incremental annotation with direct visual feedback on how labels distribute across the feature space, both within and across different feature representations. For fully-labeled data, the tool enables evaluating class separa- bility or feature consistency, as well as label revision for data quality assurance or relabeling. Currently, TSExplorer provides five widget types: audio, video, scatter, waveform, and spectrogram. The GUI layout is fully customizable, allowing users to modify widget size, loca- tion, and type, and to use multiple instances of the same widget. Each widget also contains configurable settings to support dif- ferent use cases. For example, in the waveform widget, users can control waveform grouping, number of plots, spacing, col- ors, line thickness, axis labels, among other options as well. In addition to customizing existing widgets, TSExplorer is de- signed to be extensible: new widgets, 2DV methods, and algo- rithmic sample selection strategies can be added, and existing components can be modified in a straightforward manner. 3. System Design and Implementation Figure 2 illustrates the overall system structure. TSExplorer was designed with cross-platform compatibility and usage sim- plicity in mind, and it runs on Windows, Linux, and macOS systems. The tool is implemented in Python using PySide6 for the GUI and PyQtGraph for data visualization, and audio and video playback are handled using the VLC Media Player. TSExplorer has modest computational and memory re- quirements, as feature representations are computed offline. During use, the backend primarily performs file input/output, with 2DV computation being the most demanding process. For example, computing a t-SNE embedding for 100,000 samples (160-dimensional) requires approximately 1.8 GB of RAM and 8 minutes on a single CPU core (3.9 GHz). To avoid repeated computation, TSExplorer saves the computed 2DVs, and pre- computing them is recommended for large datasets to reduce computational overhead. Memory usage scales with dataset size and feature configuration: For example, a speech dataset con- taining 100,000 samples (mean utterance duration 1.5 s) with pre-loaded audio and log-mel, MFCC, and F0 features and their corresponding 2DVs uses 5.1 GB of RAM. In contrast, having audio as individual files on disk and loading samples on demand reduces RAM usage below 0.4 GB. With no data loaded, TSEx- plorer idles at approximately 0.3 GB of RAM. 4. Acknowledgments The authors are grateful to Santeri Heiskanen for developing the initial version of TSExplorer. This work was supported in part by the Research Council of Finland (grants no. 343498 and 371243) and in part by the Sigrid Jus ́ elius Foundation. The author Einari Vaaras would also like to thank the Finnish Foun- dation for Technology Promotion for the encouragement grants and the Nokia Foundation for the Nokia Scholarship. 5. Generative AI Use Disclosure Generative AI was used only in a limited role for programming support, such as syntax guidance and debugging. 6. References [1] A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Represen- tations,” in Proc. NeurIPS, 2020, p. 12 449–12 460. [2] A. Chatzimparmpas, R. M. Martins, and A. Kerren, “t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 8, p. 2696–2714, 2020. [3] Y. Yang, H. Sun, Y. Zhang, T. Zhang, J. Gong, Y. Wei, Y.-G. Duan, M. Shu, Y. Yang, D. Wu, and D. Yu, “Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk tran- scriptomic data,” Cell Reports, vol. 36, no. 4, p. 109442, 2021. [4] I. Stolarek, A. Samelak-Czajka, M. Figlerowicz, and P. Jack- owiak, “Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data,” iScience, vol. 25, no. 10, p. 105142, 2022. [5] P. Fallgren, Z. Malisz, and J. Edlund, “How to Annotate 100 Hours in 45 Minutes,” in Proc. Interspeech, 2019, p. 341–345. [6] E. Vaaras, M. Airaksinen, and O. R ̈ as ̈ anen, “Evaluating interac- tive 2D visualization as a sample selection strategy for biomedical time-series data annotation,” Computers in Biology and Medicine, vol. 213, p. 111809, 2026. [7] S. R. Livingstone and F. A. Russo, “The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dy- namic, multimodal set of facial and vocal expressions in North American English,” PLoS ONE, vol. 13, no. 5, p. e0196391, 2018. [8] L. van der Maaten and G. Hinton, “Visualizing High-Dimensional Data Using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 86, p. 2579–2605, 2008. [9] L. McInnes, J. Healy, and J. Melville, “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction,” arXiv preprint arXiv: 1802.03426, 2018. [10] D. J. Rosenkrantz, R. E. Stearns, and P. M. Lewis, I, “An Anal- ysis of Several Heuristics for the Traveling Salesman Problem,” SIAM Journal on Computing, vol. 6, no. 3, p. 563–581, 1977.