An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

Real-time magnetic resonance imaging (rtMRI) is a technique that provides high-contrast videographic data of human anatomy in motion. Applied to the vocal tract, it is a powerful method for capturing the dynamics of speech and other vocal behaviours by imaging structures internal to the mouth and throat. These images provide a means of studying the physiological basis for speech, singing, expressions of emotion, and swallowing that are otherwise not accessible for external observation. However, taking quantitative measurements from these images is notoriously difficult. We introduce a signal processing pipeline that produces outlines of the vocal tract from the lips to the larynx as a quantification of the dynamic morphology of the vocal tract. Our approach performs simple tissue classification, but constrained to a researcher-specified region of interest. This combination facilitates feature extraction while retaining the domain-specific expertise of a human analyst. We demonstrate that this pipeline generalises well across datasets covering behaviours such as speech, vocal size exaggeration, laughter, and whistling, as well as producing reliable outcomes across analysts, particularly among users with domain-specific expertise. With this article, we make this pipeline available for immediate use by the research community, and further suggest that it may contribute to the continued development of fully automated methods based on deep learning algorithms

Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario. Specifically, we are interested in spotting the presence of horns, and sirens of emergency vehicles. In order to obtain a reliable system able to operate robustly despite the presence of traffic noise, which can be copious, unstructured and unpredictable, we propose to treat the spectrograms of incoming stereo signals as images, and apply semantic segmentation, based on a Unet architecture, to extract the target sound from the background noise. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the horizon plane, by regressing the direction of arrival of the sound through a CNN architecture. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of 2.5{\deg} when operating on frames of 2.5s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.Comment: 6 pages, 9 figure

Video Copy Detection Utilizing Log-Polar Transformation

Video Copy Detection is the process of comparing two videos to determine their similarity and determine if they are copies. This thesis enhances some of the common algorithms used in Video Copy Detection by utilizing the Log-Polar transformation as a pre-processing step. This pre-processing step is expected to increase speed of the overall Video Copy Detection process while maintaining the accuracy of the algorithms. The results of this research show that the addition of a Log-Polar pre-processing step reduces the computation time of the overall Video Copy Detection process. The additional time necessary to perform the Log-Polar pre-processing step is outweighed by the overall reduction in computation time. The accuracy and recall are slightly affected by the addition of this pre-processing step. The results also show that the video frame size can be significantly compressed with minimal effect to the algorithm\u27s overall performance

Spatial sound for computer games and virtual reality

In this chapter, we discuss spatial sound within the context of Virtual Reality and other synthetic environments such as computer games. We review current audio technologies, sound constraints within immersive multi-modal spaces, and future trends. The review process takes into consideration the wide-varying levels of audio sophistication in the gaming and VR industries, ranging from standard stereo output to Head Related Transfer Function implementation. The level of sophistication is determined mostly by hardware/system constraints (such as mobile devices or network limitations), however audio practitioners are developing novel and diverse methods to overcome many of these challenges. No matter what approach is employed, the primary objectives are very similar—the enhancement of the virtual scene and the enrichment of the user experience. We discuss how successful various audio technologies are in achieving these objectives, how they fall short, and how they are aligned to overcome these shortfalls in future implementations