Sperm whales communicate in short sequences of clicks known as codas. We present WhAM (Whale Acoustics Model), the first transformer-based model capable of generating synthetic sperm whale codas from any audio prompt. WhAM is built by finetuning VampNet, a masked acoustic token model pretrained on musical audio, using 10k coda recordings collected over the past two decades. Through iterative masked token prediction, WhAM generates high-fidelity synthetic codas that preserve key acoustic features of the source recordings. We evaluate WhAM's synthetic codas using Fréchet Audio Distance and through perceptual studies with expert marine biologists. On downstream classification tasks including rhythm, social unit, and vowel classification, WhAM's learned representations achieve strong performance, despite being trained for generation rather than classification. Our code is available at https://github.com/Project-CETI/wham

Scientists decoding whale clicks found patterns that echo the building blocks of human speech. The marine mammals have a complex communication system that scientists are working to decode. Breakthroughs, discoveries, and DIY tips sent every weekday. A new study discovered a fresh component of their various vocalizations and could hint at potential language structures. Sperm whales exhibit patterns similar to human vowels and diphthongs-a connected pair of vowels in a word, such as the "oi" in .

Marine biologists are inching closer to understanding the ins and outs of sperm whale communication. But in order to decode what the cetaceans are saying, they must first need to find them and know where they will surface. This is no easy feat, since sperm whales can dive over 10,000 feet andstay way below the surface for up to 60 minutes. Their habitats themselves stretch for thousands of miles. Now, scientists from Project CETI (Cetacean Translation Initiative) and Harvard University are proposing a new method for finding sperm whales and predicting where they will surface using autonomous robots and a rich combination of sensor data.

Scientists studying sperm whales have discovered that they communicate through a sort of "phonetic alphabet", enabling them to build a rough equivalent of what humans refer to as words and phrases. The study, published on Tuesday, involved sperm whales living around the Caribbean island of Dominica, describing how they communicate by squeezing air through their respiratory systems to make rapid clicks resembling Morse code, with sets of the noises making up the basic building blocks of language. Research showed the "expressivity" of sperm whale calls was bigger than previously thought, said Pratyusha Sharma, a lead author of the study, which was published in the journal Nature Communications. "We do not know yet what they are saying. We are studying the calls in their behavioural contexts next to understand what sperm whales might be communicating about," she said.

Sperm whales (Physeter macrocephalus) navigate underwater with a series of impulsive, click-like sounds known as echolocation clicks. These clicks are characterized by a multipulse structure (MPS) that serves as a distinctive pattern. In this work, we use the stability of the MPS as a detection metric for recognizing and classifying the presence of clicks in noisy environments. To distinguish between noise transients and to handle simultaneous emissions from multiple sperm whales, our approach clusters a time series of MPS measures while removing potential clicks that do not fulfil the limits of inter-click interval, duration and spectrum. As a result, our approach can handle high noise transients and low signal-to-noise ratio. The performance of our detection approach is examined using three datasets: seven months of recordings from the Mediterranean Sea containing manually verified ambient noise; several days of manually labelled data collected from the Dominica Island containing approximately 40,000 clicks from multiple sperm whales; and a dataset from the Bahamas containing 1,203 labelled clicks from a single sperm whale. Comparing with the results of two benchmark detectors, a better trade-off between precision and recall is observed as well as a significant reduction in false detection rates, especially in noisy environments. To ensure reproducibility, we provide our database of labelled clicks along with our implementation code.

Dana Goodyear's article about the scientist He Jiankui captured the terrifying potential of gene editing ("Dangerous Designs," September 11th). However, many of the problems that certain scientists are trying to remedy with the gene-editing tool CRISPR already have controversy-free solutions. Conditions such as Batten disease, caused when a baby inherits one pathogenic gene from each parent, can be prevented by pre-conception screenings of would-be parents. Hypertrophic cardiomyopathy, which is inherited from one parent, can be averted using pre-implant genetic diagnosis, a common I.V.F. Gene editing will eventually have a place in clinical medicine, but its use will likely be minimal, compared with these currently accessible, effective, and less buzzworthy techniques.

David Gruber began his almost impossibly varied career studying bluestriped grunt fish off the coast of Belize. He was an undergraduate, and his job was to track the fish at night. He navigated by the stars and slept in a tent on the beach. "It was a dream," he recalled recently. "I didn't know what I was doing, but I was performing what I thought a marine biologist would do."

This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values outside the training range with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this approach yields insights for model interpretability. Using this technique, we can infer what properties of unknown data the model encodes as meaningful. We apply the methodology to test what is meaningful in the communication system of sperm whales, one of the most intriguing and understudied animal communication systems. We train a network that has been shown to learn meaningful representations of speech and test whether we can leverage such unsupervised learning to decipher the properties of another vocal communication system for which we have no ground truth. The proposed technique suggests that sperm whales encode information using the number of clicks in a sequence, the regularity of their timing, and audio properties such as the spectral mean and the acoustic regularity of the sequences. Some of these findings are consistent with existing hypotheses, while others are proposed for the first time. We also argue that our models uncover rules that govern the structure of communication units in the sperm whale communication system and apply them while generating innovative data not shown during training. This paper suggests that an interpretation of the outputs of deep neural networks with causal methodology can be a viable strategy for approaching data about which little is known and presents another case of how deep learning can limit the hypothesis space. Finally, the proposed approach combining latent space manipulation and causal inference can be extended to other architectures and arbitrary datasets.

AI is being applied to whale research, especially to understand what whales are trying to communicate in the audible sounds they make to each other in the ocean. For example, marine biologist Shane Gero has worked to match clicks coming from whales around the Caribbean island nation of Dominica, to behavior he hopes will reveal the meanings of the sounds they make. Gero is a behavioral ecologist affiliated with the Marine Bioacoustics Lab at Aarhus University in Denmark, and the Department of Biology of Dalhousie University of Halifax, Nova Scotia. Gero works with a team from Project CETI, a nonprofit that aims to apply advanced machine learning and state-of-the-art robotics to listen to and translate the communication of whales. Project CETI has recently announced a five-year effort to build on Gero's work with a research project to try to decipher what sperm whales are saying to each other, according to a recent account in National Geographic.

With the goal of'talking' to the majestic marine animals, a project to listen to, contextualise and translate the communication of Sperm whales was recently launched by a team of international scientists. Called Project CETI (Cetacean Translation Initiative), the initiative looks to harness the power of artificial intelligence to interpret clicking sounds, or'codas,' which these whales make to communicate with one another. Also Read: Why is mysterious dark energy leading to expansion of the universe? The researchers have been using natural language processing or NLP, a subfield of artificial intelligence focused on processing written and spoken human language. It will be trained in four billion Sperm whale codas.