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Register a new userAudioViewer: Learning to Visualize Sound
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Added by
Yuchi Zhang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Sensory substitution can help persons with perceptual deficits. In this work, we attempt to visualize audio with video. Our long-term goal is to create sound perception for hearing impaired people, for instance, to facilitate feedback for training deaf speech. Different from existing models that translate between speech and text or text and images, we target an immediate and low-level translation that applies to generic environment sounds and human speech without delay. No canonical mapping is known for this artificial translation task. Our design is to translate from audio to video by compressing both into a common latent space with shared structure. Our core contribution is the development and evaluation of learned mappings that respect human perception limits and maximize user comfort by enforcing priors and combining strategies from unpaired image translation and disentanglement. We demonstrate qualitatively and quantitatively that our AudioViewer model maintains important audio features in the generated video and that generated videos of faces and numbers are well suited for visualizing high-dimensional audio features since they can easily be parsed by humans to match and distinguish between sounds, words, and speakers.
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Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile applications, and online websites. A good GUI design is crucial to the success of the software in the market, but designing a good GUI which requires much innovation and creativity is difficult even to well-trained designers. Besides, the requirement of the rapid development of GUI design also aggravates designers working load. So, the availability of various automated generated GUIs can help enhance the design personalization and specialization as they can cater to the taste of different designers. To assist designers, we develop a model GUIGAN to automatically generate GUI designs. Different from conventional image generation models based on image pixels, our GUIGAN is to reuse GUI components collected from existing mobile app GUIs for composing a new design that is similar to natural-language generation. Our GUIGAN is based on SeqGAN by modeling the GUI component style compatibility and GUI structure. The evaluation demonstrates that our model significantly outperforms the best of the baseline methods by 30.77% in Frechet Inception distance (FID) and 12.35% in 1-Nearest Neighbor Accuracy (1-NNA). Through a pilot user study, we provide initial evidence of the usefulness of our approach for generating acceptable brand new GUI designs.
Clear presentation of uncertainty is an exception rather than rule in media articles, data-driven reports, and consumer applications, despite proposed techniques for communicating sources of uncertainty in data. This work considers, Why do so many visualization authors choose not to visualize uncertainty? I contribute a detailed characterization of practices, associations, and attitudes related to uncertainty communication among visualization authors, derived from the results of surveying 90 authors who regularly create visualizations for others as part of their work, and interviewing thirteen influential visualization designers. My results highlight challenges that authors face and expose assumptions and inconsistencies in beliefs about the role of uncertainty in visualization. In particular, a clear contradiction arises between authors acknowledgment of the value of depicting uncertainty and the norm of omitting direct depiction of uncertainty. To help explain this contradiction, I present a rhetorical model of uncertainty omission in visualization-based communication. I also adapt a formal statistical model of how viewers judge the strength of a signal in a visualization to visualization-based communication, to argue that uncertainty communication necessarily reduces degrees of freedom in viewers statistical inferences. I conclude with recommendations for how visualization research on uncertainty communication could better serve practitioners current needs and values while deepening understanding of assumptions that reinforce uncertainty omission.
Human-computer interaction (HCI) is crucial for the safety of lives as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs understand humans on the road. In this paper, we present GLADAS, a simulator-based research platform designed to teach AVs to understand pedestrian hand gestures. GLADAS supports the training, testing, and validation of deep learning-based self-driving car gesture recognition systems. We focus on gestures as they are a primordial (i.e, natural and common) way to interact with cars. To the best of our knowledge, GLADAS is the first system of its kind designed to provide an infrastructure for further research into human-AV interaction. We also develop a hand gesture recognition algorithm for self-driving cars, using GLADAS to evaluate its performance. Our results show that an AV understands human gestures 85.91% of the time, reinforcing the need for further research into human-AV interaction.
Data visualization should be accessible for all analysts with data, not just the few with technical expertise. Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select, rather than manually specify. Here, we demonstrate a novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. First, we identify five key design choices made by analysts while creating visualizations, such as selecting a visualization type and choosing to encode a column along the X- or Y-axis. We train models to predict these design choices using one million dataset-visualization pairs collected from a popular online visualization platform. Neural networks predict these design choices with high accuracy compared to baseline models. We report and interpret feature importances from one of these baseline models. To evaluate the generalizability and uncertainty of our approach, we benchmark with a crowdsourced test set, and show that the performance of our model is comparable to human performance when predicting consensus visualization type, and exceeds that of other ML-based systems.
Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however, we have often ignored a key sense: sound. This is primarily due to the lack of data that captures the interplay of action and sound. In this work, we perform the first large-scale study of the interactions between sound and robotic action. To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information. Using this data, we explore the synergies between sound and action and present three key insights. First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench. Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied to the object. Finally, object representations derived from audio embeddings are indicative of implicit physical properties. We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings. Project videos and data are at https://dhiraj100892.github.io/swoosh/
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