Leora Robinson – leorarobinson13@gmail.com
Brigham Young University
Provo, UT 84602
United States
Tracianne Neilsen
Popular version of 3pUW6 – Acoustic binary classification of ice cover conditions using deep learning
Presented at the 190th ASA Meeting
Read the abstract at https://eppro01.ativ.me/web/index.php?page=session&project=ASASPRING2026&id=4082831
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Some Arctic animals don’t need to see ice to find it—they can hear it. Species like the beluga whale use sound to navigate through icy waters where visibility is limited, finding breathing holes in the ice without ever seeing them. This project asks a simple question: Can a computer learn to do the same? By analyzing acoustic signals, we show that a neural network can detect ice without relying on visual information.
Initial experiments were conducted in a laboratory tank (Figure 1) at the Brigham Young University Department of Physics and Astronomy. We took sound recordings when ice was and was not present on the surface of the water. Then, we trained a machine learning classifier to label the recordings as ‘ice’ or ‘no ice.’
Figure 1. Laboratory tank (side view).
For these experiments, we placed an underwater loudspeaker (transmitter) and an underwater microphone (hydrophone) in the tank. The transmitter produced ultrasonic chirps of increasing frequency when ice was and wasn’t present. We added about 600 pounds of block ice to the tank and took one-second recordings before ice was added, while it was present, and after it melted. We took two additional sets of recordings for testing the neural network: one using block ice and one using pebble ice.
After we acquired the recordings, we needed to label them. We did this using camera footage of the tank (Figure 2). Recordings with about 5% or more ice coverage between the transmitter and the hydrophone were labeled ‘ice,’ and recordings with less than 5% coverage were labeled ‘no ice.’ We chose this 5% threshold to differentiate between negligible and non-negligible ice cover. We converted each labeled recording into a time-frequency spectrogram and used the spectrograms to train a machine learning classifier.
Figure 2. Camera footage of the laboratory tank for labeling.
Figure 2. Camera footage of the laboratory tank for labeling.For the machine learning classifier, we selected a convolutional neural network (CNN) because it can detect important features indicating the presence of ice. We passed the spectrograms and their associated labels through the classifier for training, where the CNN learned to associate certain features of spectrograms with their labels. Ten classifiers were trained to provide a statistical representation of performance.
Figure 3. Roadmap of how each audio recording was processed and classified.
Figure 3. Roadmap of how each audio recording was processed and classified.Once the ten classifiers were trained, we tested their performance on two other datasets that they were not trained on. We did this to see how well the CNN could generalize to other conditions. This generalizability is important because, in practical applications, the ocean environment is always changing: no two recordings will ever have identical conditions. The mean labeling accuracy across the ten classifiers on the testing block ice dataset was 93.5% ± 0.9%. On the pebble ice dataset, the classifiers achieved 94.3% ± 1.4% accuracy. These tests show that the CNNs can generalize well to new conditions.
The high accuracy of these initial experiments indicates that a CNN can use sound to detect the presence of ice. Just as the beluga whale listens for audio cues to find breathing holes in the ice, the neural network extracts important information from the sound to determine whether ice is present.