From active to passive spatial acoustic sensing and applications




Sun, Wei (Ph. D. in computer science)

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The active acoustic sensing system emits modulated acoustic waves and analyzes reflection signals. It is dominant in acoustic spatial sensing. On the other side, the passive acoustic sensing system receives and investigates nature sounds directly. It is good at semantic tasks but has weak performance on spatial sensing. In this dissertation, we manage to bridge three gaps in existing systems. They are the gap between the assumption of signal processing algorithms and the real acoustic environment, the gap between powerful active spatial sensing and limited passive spatial sensing, and the gap between the semantic features and spatial information. We evolve the acoustic sensing system design and extend the functionalities by three novel systems.

First, we develop a fully active spatial sensing system DeepRange which can adapt to the real environment easily. We develop an effective mechanism to generate synthetic training data that captures noise, speaker/mic distortion, and interference in the signals. It removes the need of collecting a large volume of data. We then design a deep range neural network (DRNet) to estimate the distance from raw acoustic signals. It is inspired by signal processing that an ultra-long convolution kernel size helps to combat noise and interference. The model is fully trained over synthetic data, but it can achieve sub-centimeter error robustly in real data despite various environments, background noise, interference, and mobile phone models.

Second, we develop a fused active and passive spatial sensing system for speech separation noted as Spatial Aware Multi-task learning-based Separation (SAMS). We leverage both active sensing and passive sensing to improve AoA estimation and jointly optimize the semantic task and the spatial task. SAMS estimates the spatial location and extracts speech for the target user during teleconferencing simultaneously. We first generate fine-grained spatial embeddings from the user’s voice and inaudible tracking sound, which contains the user’s position and rich multipath information. Furthermore, we develop a deep neural network with multi-task learning to jointly optimize source separation and location. We significantly speed up inference to provide a real-time guarantee.

Finally, we deeply fuse the semantic features and spatial cues to combat the interference and noise in the real environment as well as enable depth sensing in a fully passive setup. Inspired by the ”flash-to-bang” phenomenon (i.e.hearing the thunder after seeing the lightning), we propose FBDepth to measure the depth of the sound source. We formulate the problem as an audio-visual event localization task for collision events. Specifically, FBDepth first aligns correspondence between the video track and audio track to locate the target object and target sound in a coarse granularity. Based on the observation of moving objects’ trajectories, it proposes to estimate the intersection of optical flow before and after the collision to locate video events in time. It feeds the estimated timestamp of the video event and the other modalities for the final depth estimation. We use a mobile phone to collect the 3.6K+ video clips involving 24 different objects at up to 60m. FBDepth shows superior performance especially at a long range compared to monocular and stereo methods.


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