Sadly, you're just a couple of months too early to read an upcoming volume of scientific papers specifically addressing the issue of xenolinguistics and theoretical issues with fieldwork on alien languages....
In the meantime, a lot depends on what the aliens look like. The fact that their language is pronounceable by humans is an amazingly unlikely and super convenient coincidence which means that all of the tools of monolingual fieldwork can be brought into play. The movie Arrival does a good job of illustrating the basics, in a much more challenging situation than what you have described. However, the more human-like the aliens are, the smoother it will go, as the linguists involved will be able to rely more heavily on things like assuming that the aliens will have analogous words for analogous body parts and understand pantomime in similar ways as us. If their bodies are radically different, there will have to be a longer stage of figuring out how to understand their body language, and teaching them to understand ours. Note that this is still an issue for human-targeted fieldwork anyway, as gestures are not universal, but it will be a bigger issue when you're not at least starting with bodies of the same shape.
It can be assumed that the aliens can hear at least the same bands of frequencies in which they voices exist, but not outside that. Similarly, no assumptions can be made about their other sensory capacities. Maybe they see a different spectral range from us. Maybe they don't see color. Maybe they don't see at all. Maybe they pay more attention to scent than we do. To cover all of those options in preparation for elicitation work, teams of researchers would be compiling a wide variety of different types of stimulus materials to determine what the aliens can distinguish and what they care about as groundwork for more targeted elicitation later.
Absolutely everything that the aliens say should be recorded, in the highest possible fidelity, so as to capture any distinctions that may not be obvious to not-yet-trained ears. This will be accompanied by video recordings to provide context for each utterance. For everyone interacting directly with the aliens, there will be five or ten who just do data analysis on the recordings that come out of elicitation sessions. While none of these are done in isolation, and elicitation experts will figure stuff out about multiple organizational levels at once, the first issue for analysts will be identifying contrasting phonemes--which might be possible ahead of time based solely on the corpus of transmissions--then establishing a transcription convention, identifying "words" (roots, collocations, idioms, etc.), and then finally building up successively more complex levels of grammar.
And while nobody will strongly expect anything to come of it, someone is going to try zero-shot learning by producing an embedding vector space on the tokenized corpus of recorded alien speech and trying to correlate it with equal-dimensional word-vector spaces for major human languages. It's relatively cheap, and hey, you might get lucky.
Edit, to explain the last paragraph:
Zero-shot learning: learning to classify inputs that belong to categories the learning system has never examples of before, based on correlating knowledge from multiple other sources. I.e., a zero-shot image classifiers might be able to correctly identify pictures of zebras without ever having been trained on zebras because it knows what stripes are, and knows what horses are, and has been told that a zebra looks like a striped horse.
Embedding vector space on a tokenized corpus: this is how LLMs, like ChatGPT, encode their inputs. It's a way of being able to do math on words. Basically, you come up with a method of splitting a collection of texts (a corpus) into discrete tokens (letters, words, or whatever happens to work), and then you compute a list of numbers--a vector--that represents each of those tokens based on the other tokens that it occurs in context with. The position of the resulting vectors in higher-dimensional space often correlates with useful semantic features of the tokens.
Correlating word vector spaces: zero-shot learning for machine translation is done by producing embedding vectors for multiple languages, and then looking for clusters of points that have the same shapes in each model. If you assume that the matching points are translation-equivalents, then that gives you a way to convert a semantic vector from one model into a semantic vector from the other model, and start translating languages without ever having seen a parallel text.
This technology is only proven to work at all when starting with extremely large data sets of relatively closely related languages, but it is being seriously researched to see if can be extended to provide cheap machine translation for less well-documented languages and even to decipher animal communication, like whale songs.