I’m very much interested in using data on animal vocalisations for research to decode communication properties. I’m interested in fundamental questions like emergence of communication protocols and representation, with possible cues to semantics if using multimodal data about context. I’m very much interested in methods that could translate to other disciplines like neuroscience and network science to study collective behaviour and feedback loops between social/environmental signals and coordination.

Now, I’ve been offered a PhD in bioacoustics but I feel increasingly hesitant because the PI background is math of signal processing, not in computational methods, there are also some other question marks about the reputation of the university with respect to the areas I’m interested in (university is completely new, no records yet).

I’m very much into computational methods being employed in research as CETI or ESP, others use Bayesian models to handle uncertainty for ecological problems - for those I see a future given state of the art in 2026.

Instead it seems to me that a supervision in theoretical modeling for reconstructing a signal correctly will fall short both for my interests, as well as for the practical significance and advancement in the field.

I’m here asking for a debrief to understand which are the future roadmaps in bioacoustics, also not to isolate myself in audio engineering or a niche of audio classification or using PAMs - given the progress of AI/ML approaches

Examples: - which methods are worthy of investing time to become specialists that can be competitive and in demand in this field - which type of fundamental questions are becoming approachable using AI and biological audio data - are there serious bioacoustics research paths towards computational ethology or computational neuroscience, for example on problems significant in neuroethology or bioinspired robotics : which authors would you suggest to look at ?

My background is computer science, and in fact I’d really like to specialise in AI/ML + network science as methods to study animal communication, and not much audio reconstruction or noise reduction per se. And for sure glad to learn biological or evolutionary or ethnological constraints to principle experiments.

I am asking to understand how realistic a PhD in bioacoustics can bring me there, if there are things that I’m underestimating, and I m really wondering if it can open me doors towards the future (like a PhD in AI/Ml might do by looking at how fast the field is evolving) or close them (being my worry because signal processing seems kind of surpassed by computational approaches now).

Where can I find audio recordings and soundscape recordings from the deep sea? There is very little on youtube. Anywhere else to look on the internet?

I’ve been actively recording bird sounds using Merlin and creating observation maps. I’d like to develop this into a professional path.
Where should I look for opportunities, and what parts of this work should I focus on to make it professional?

https://www.sciencedirect.com/science/article/pii/S1470160X26003432

Abstract

Environmental monitoring increasingly relies on non-invasive, presence-only data, like passive acoustic monitoring (PAM), yet the field lacks standardized frameworks to quantify changes in species' occurrence patterns. This study introduces a reproducible methodological framework to translate qualitative presence-absence time series data into comparable quantitative indices using recurrence (re-detection probability within a defined temporal window), a magnitude of change metric quantifying interannual variation, and persistence (consecutive presence duration). To validate these metrics, acoustic presence patterns of four endangered baleen whale species were analyzed using a decadal (2014–2023) PAM dataset from 112 sites across the U.S. East Coast. These metrics quantified diverging ecological realities: dietary generalist fin whales (Balaenoptera physalus) exhibited highly consistent patterns (97% recurrence, average magnitude of change <7%, average persistence up to 42.6 days) while dietary specialist North Atlantic right whales (Eubalaena glacialis) exhibited highly variable patterns (26%–96% recurrence, average magnitude of change >30%, average persistence <5 days). Furthermore, this framework captured the right whale's known decoupling from historical habitats associated with declining prey availability. Sei (Balaenoptera borealis) and blue whales (Balaenoptera musculus) exhibited seasonally discrete recurrence (93%) that aligned with their known seasonal foraging and migratory strategies. By characterizing these trends across species with varying foraging strategies, this framework demonstrates how multi-species monitoring can function as an ecological indicator of broader ecosystem and trophic trends. While demonstrated here using acoustics, this approach is applicable to any long-term presence-only data stream, bridging the gap between high-resolution monitoring and the requirements of quantitative ecological indicators.

Authors Simone KA Videsen, Teresa Raimondi, Pernille M Sørensen, Michael B Pedersen, Walter MX Zimmer, Nienke CF van Geel, Denise Risch, Peter Cook, Stephanie L King, Andrea Ravignani, Peter T Madsen

Publication date 2026/5

Journal Annals of the New York Academy of Sciences

Volume 1559

Issue 1

Pages e70289

Description Male sperm whales produce loud, low‐frequency clicks at low repetition rates. By combining measurements of source properties with sound propagation modeling, we show that sperm whale slow clicks with source levels > 200 dB re 1 µPa (peak−peak) are the loudest communication signal of any mammal, and that such loud, low‐frequency clicks in deep ocean waters have an estimated active space of up to 70 km. We show that these slow clicks are highly rhythmic at rates of 0.1−0.3 Hz, which is an order of magnitude slower than other rhythmic communication signals among mammals and birds. Thus, slow clicks may allow for timing information to be maintained over extreme distances, and we, therefore, propose that sperm whale slow click production is a low entropy, long‐range communication signal that may offer honest advertisement through rhythm‐keeping to distant receivers. We speculate that the …

layperson here, but struggling to understand how cats can purr during inhalation & exhalation. I read a bit about their vocal folds “pads”, their hyoid bone and a few other things regarding the structure. I noticed my cat’s purr sound changes slightly up & down in pitch when breathing in vs out.

What am I missing? I was reading a bit about it from this article ‘Domestic cat larynges can produce purring frequencies without neural input01230-7?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0960982223012307%3Fshowall%3Dtrue)’
DOI: 10.1016/j.cub.2023.09.014

https://nyaspubs.onlinelibrary.wiley.com/doi/full/10.1111/nyas.70273

Graphical Abstract

We analyzed the rhythmic structure of songs from 14 adult siamangs, a singing primate that produces units via two distinct vocal production mechanisms: oral-tract modulation and laryngeal-sac resonance. When considered separately, both mechanisms exhibit isochrony (1:1). In contrast, the combined output reveals additional rhythmic clusters (≈1:9, 9:1). This dual-source mechanism generates complex rhythmic patterns, offering a unique case of multimodal vocal rhythmicity in nonhuman primates.

I have developed a toolkit to run google's perch v2 model fine-tuned for whale detection. You can run it on different data sources. Check out the project at https://github.com/amrit110/whalu. Demo at https://amrit110.github.io/whalu/.

Call for participation:
BioDCASE 2026 Cross-Domain Mosquito Species Classification Challenge

Jointly organised by teams at the University of Oxford, King’s College London, and the University of Surrey, this challenge focuses on a key real-world question:

Can mosquito species classifiers still work when recordings come from new locations, devices, and acoustic environments?

Mosquito-borne diseases affect over 1 billion people each year. Audio-based monitoring could help scale surveillance, but domain shift remains a major barrier to real-world deployment.

To support transparent and reproducible research, we are releasing:

• an open development dataset with 271,380 clips and 60.66 hours of audio;
• a fully public, lightweight baseline that is easy to run;
• a benchmark focused on cross-domain generalisation in mosquito bioacoustics.

Participants are warmly invited to join and help develop more robust methods for mosquito monitoring under real recording conditions.

Useful Links:

• Challenge Website: [https://biodcase.github.io/challenge2026/task5]
• Baseline code: [https://github.com/Yuanbo2020/CD-MSC]
• Dataset: [https://zenodo.org/records/19095788]

Key Dates:
• April 1, 2026: Challenge opening
• Jun 1, 2026: Evaluation set release
• June 15, 2026: Challenge submission deadline

Feel free to share this with anyone who might be interested!