Underwater 3D positioning on smart devices

The emergence of water-proof mobile and wearable devices (e.g., Garmin Descent and Apple Watch Ultra) designed for underwater activities like professional scuba diving, opens up opportunities for underwater networking and localization capabilities on these devices. Here, we present the first underwater acoustic positioning system for smart devices. Unlike conventional systems that use floating buoys as anchors at known locations, we design a system where a dive leader can compute the relative positions of all other divers, without any external infrastructure. Our intuition is that in a well-connected network of devices, if we compute the pairwise distances, we can determine the shape of the network topology. By incorporating orientation information about a single diver who is in the visual range of the leader device, we can then estimate the positions of all the remaining divers, even if they are not within sight. We address various practical problems including detecting erroneous distance estimates, addressing rotational and flipping ambiguities as well as designing a distributed timestamp protocol that scales linearly with the number of devices. Our evaluations show that our distributed system running on underwater deployments of 4-5 commodity smart devices can perform pairwise ranging and localization with median errors of 0.5-0.9 m and 0.9-1.6 m

Real-Time Target Sound Extraction

We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture with a stack of dilated causal convolution layers as the encoder, and a transformer decoder layer as the decoder. This hybrid architecture uses dilated causal convolutions for processing large receptive fields in a computationally efficient manner, while also benefiting from the performance transformer-based architectures provide. Our evaluations show as much as 2.2-3.3 dB improvement in SI-SNRi compared to the prior models for this task while having a 1.2-4x smaller model size and a 1.5-2x lower runtime. Open-source code and datasets: https://github.com/vb000/Waveformer

Underwater Messaging Using Mobile Devices

Since its inception, underwater digital acoustic communication has required custom hardware that neither has the economies of scale nor is pervasive. We present the first acoustic system that brings underwater messaging capabilities to existing mobile devices like smartphones and smart watches. Our software-only solution leverages audio sensors, i.e., microphones and speakers, ubiquitous in today's devices to enable acoustic underwater communication between mobile devices. To achieve this, we design a communication system that in real-time adapts to differences in frequency responses across mobile devices, changes in multipath and noise levels at different locations and dynamic channel changes due to mobility. We evaluate our system in six different real-world underwater environments with depths of 2-15 m in the presence of boats, ships and people fishing and kayaking. Our results show that our system can in real-time adapt its frequency band and achieve bit rates of 100 bps to 1.8 kbps and a range of 30 m. By using a lower bit rate of 10-20 bps, we can further increase the range to 100 m. As smartphones and watches are increasingly being used in underwater scenarios, our software-based approach has the potential to make underwater messaging capabilities widely available to anyone with a mobile device. Project page with open-source code and data can be found here: https://underwatermessaging.cs.washington.edu/