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Array signal processing

The popularity of smartphones has grown at an unprecedented rate, which makes smartphone based imaging especially appealing. In this paper, we develop a novel acoustic imaging system using only an off-the-shelf smartphone. It is an attractive alternative to camera based imaging under darkness and obstruction. Our system is based on Synthetic Aperture Radar (SAR). To image an object, a user moves a phone along a predefined trajectory to mimic a virtual sensor array. SAR based imaging poses several new challenges in our context, including strong self and background interference, deviation from the desired trajectory due to hand jitters, and severe speaker/microphone distortion. We address these challenges by developing a 2-stage interference cancellation scheme, a new algorithm to compensate trajectory errors, and an effective method to minimize the impact of signal distortion. We implement a proof-of-concept system on Samsung S7. Our results demonstrate the feasibility and effectiveness of acoustic imaging on a mobile.

AIM: Acoustic Imaging on a Mobile

This paper introduces SuperRF- which takes radio frequency (RF) signals from an off-the-shelf, low-cost, 77GHz mmWave radar and produces an enhanced 3D RF representation of a scene. SuperRF is useful in scenarios where camera and other types of sensors do not work, or not allowed due to privacy concerns, or their performance is impacted due to bad lighting conditions and occlusions, or an alternate RF sensing system like synthetic aperture radar (SAR) is too large, inconvenient, and costly. Applications of SuperRF includes navigation and planning of autonomous and semi-autonomous systems, human-robot interactions and social robotics, and elderly and/or patient monitoring in-home healthcare scenarios. We use low-cost, off-the-shelf parts to capture RF signals and to train SuperRF. The novelty of SuperRF lies in its use of deep learning algorithm, followed by a compressed sensing-based iterative algorithm that further enhances the output, to generate a fine-grained 3D representation of an RF scene from its sparse RF representation - which a mmWave radar of the same class cannot achieve without instrumenting the system with large sized multiple antennas or physically moving the antenna over a longer period in time. We demonstrate the feasibility and effectiveness through an in-depth evaluation.

SuperRF: Enhanced 3D RF Representation Using Stationary Low-Cost mmWave Radar.

Determination of frequency response of MEMS microphone from sound field measurements using optical phase-shifting interferometry method

Infinite-dimensional SVD for revealing microphone array’s characteristics

Generating realistic sounds for soft bodies is a challenging task due to the complexity of the interactions. Therefore, automatic audio generation based on procedural approach has become an attractive method for digital synthesis of soft-body sounds. In this paper, we present a comprehensive review in the field of procedural audio, with a special focus on synthesizing the sound of soft bodies. We first introduce the concept of procedural sound generation, including its advantages and challenges for soft-body sound synthesis. Next, we review the state-of-the-art in rigid/non-rigid-body sound synthesis techniques for computer animations and games. Thirdly, we summarize and survey a taxonomy of existing synthesis methods from previous literatures by analyzing their benefits and drawbacks for generating soft-body sounds. These methods include modal synthesis, sound texture modeling, motion-driven sound synthesis, wavelet tree learning, granular synthesis, and concatenative sound synthesis. Last but not least, we discuss several possible directions for future research in procedural soft-body sound synthesis.

Toward Generating Realistic Sounds for Soft Bodies: A Review

Procedurally-generated audio is an important method for the automatic synthesis of realistic sounds for computer animations and virtual environments. While synthesis techniques for rigid bodies have been well studied, few publications have tackled the challenges of synthesizing sounds for soft bodies. In this paper, we propose a data-driven synthesis approach to simultaneously generate audio based on certain given soft-body animations. Our method uses granular synthesis to extract a database of sound from real-world recordings and then retarget these grains of sounds based on the motion of any input animations. We demonstrate the effectiveness of this method on a variety of soft-body animations including a basketball bouncing, apple slicing, hand clapping and a jelly simulation.

https://curve.carleton.ca/system/files/etd/df8a1744-33dc-44e6-bf13-a4d5dc56bbc1/etd_pdf/0f6f1bb368c267c8c86b99a09fd72ec5/su-procedurallygeneratedaudioforsoftbodyinteractions.pdf

Procedurally-Generated Audio for Soft-Body Animations

Non-rigid Registration for Two-photon Imaging Using Triangulation and Piecewise Affine Transformation

We propose a real-time, data-driven method to synthesize the sound of typical soft bodies (such as cloth and rope) in video games. For a given soft body, we perform a geometric analysis of its shape at each frame. These data are used to calculate a variety of motion events at run time that would produce sounds. We then record a database of sounds, which contains sequences of segmented sound units. Finally, we use a concatenative synthesis method to select and synthesize the actual soft-body sounds according to the extracted motion signals. Our approach is more computationally efficient compared to existing soft-body sound synthesis methods and is compatible with any particle-based soft-body physics in video games. We implement our method in Unreal Engine 4 and demonstrate its efficiency with several examples.

Procedural Sound Generation for Soft Bodies in Video Games

Procedurally-generated audio has proven to be an effective solution to synthesize complex sound phenomena such as soft-body interactions in computer animations and games. However, the quality of synthesized audio varies depending on the kind of methods or parameters chosen. As a consequence, it is often necessary to constantly evaluate the output sound quality as it is produced, which can be a difficult task. In this paper, we address this issue by taking both subjective and objective approaches, and with a focus on synthesized soft-body audio. In particular, our subjective evaluation consists of a three-part perceptual study, where we explore the recognisability, quality, and synchronization of the simulated sound. For objective evaluation, we adapt the metrics from generative adversarial networks (GANs) that also measure the recognisability as well as quality of the sound from a different angle. Our results suggest that while both evaluation criteria are largely independent of each other in assessing the recognisability of the sound, objective evaluation tends to be a more efficient alternative to measure the output sound quality. In addition, we provide several findings from our results that can guide sound designers in synthesizing higher quality audio for soft bodies.

Subjective and Objective Evaluation of Procedurally-Generated Audio for Soft-Body Interactions

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