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REVIEW 4 major objections 6 minor 49 references

Exploring Finetuned Audio-LLM on Heart Murmur Features

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that a single finetuned audio LLM, Qwen2-Audio, can classify all 11 clinically relevant heart murmur features and outperform prior specialized models on most of them.

desk verdict First audio-LLM PCG feature study, but the abstract's '8 of 11 outperformance' is contradicted by the paper's own tables. read the letter →

arxiv 2501.13884 v1 pith:WSOP5PYU submitted 2025-01-23 eess.AS cs.AIcs.SD

Adrian Florea , Xilin Jiang , Nima Mesgarani , Xiaofan Jiang This is my paper · ORCID
classification eess.AScs.AIcs.SD
keywords audiolargelanguagemodelheartmurmurphonocardiogramfeatureclassificationQwen2-AudioSSAMBAsegmentationzero-shotlow-rankadaptation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a general-purpose audio large language model, Qwen2-Audio, can be finetuned on heart sound recordings to classify a comprehensive set of 11 expert-labeled murmur features—timing, shape, pitch, quality, and grading in both systolic and diastolic phases, plus weighted murmur status—using a single model rather than a battery of specialist classifiers. The authors argue that this approach outperforms existing deep learning baselines on 8 of the 11 features, performs comparably on the remaining 3, and, with the help of a SSAMBA-based segmentation front-end, also generalizes to unseen heart sound datasets in zero-shot normal/abnormal classification. The clinical motivation is that these murmur features are what physicians use to narrow a differential diagnosis, while most prior automated systems only output a healthy-versus-unhealthy label. The paper further claims that the model is the first to classify long-tail diastolic murmur features with limited training data.

What carries the argument

The load-bearing machinery is Qwen2-Audio, an audio LLM whose Whisper-based audio encoder turns a phonocardiogram into representations that a Qwen-7B language model conditions on to produce text answers in a multiple-choice format; it is adapted to the medical domain by low-rank adaptation (LoRA) of both the encoder and the LLM weights. A second component, SSAMBA, is a Mamba state-space audio representation model finetuned with a linear head to segment each recording into heartbeat and non-heartbeat intervals, and feeding these segments to the LLM is what gives the system robustness on unseen datasets. The multiple-choice question format, with randomly varied phrasing of each question, is the mechanism that converts the LLM's generative language modeling into a classifier over the fixed label sets of each of the 11 tasks.

What would settle it

Re-run Deep CardioSound and M2D+AST on the exact test split of the CirCor DigiScope data used here; if either baseline matches or exceeds the LLM's accuracy on the eight timing, shape, pitch, and quality features, the claimed 8-of-11 outperformance is unsupported.

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Extended reading notes

Core claim

The paper's central claim is that an audio LLM finetuned on heart sounds can jointly classify the full set of clinically used murmur features, going beyond the binary healthy-versus-unhealthy task that dominates prior work. On its test split of the CirCor DigiScope data, the authors report that the model reaches 100% accuracy on timing, shape, pitch, and quality for both systolic and diastolic murmurs when paired with the SSAMBA segmentation front-end, and that it is the first system to classify long-tail diastolic features at all. The same front-end enables zero-shot normal-versus-abnormal classification on two external heart sound datasets. The authors state the overall result as outperforming state-of-the-art methods on 8 of the 11 features and performing comparably on the remaining 3, while noting that grading features remain difficult for the model.

Load-bearing premise

The comparison assumes that the baseline accuracies from Deep CardioSound and M2D+AST were measured under the same feature definitions, accuracy metric, patient splits, and label sets as this paper's evaluation, so the numbers in Tables 1 and 2 are directly comparable.

Editorial extensions

If this is right

  • If the central claim holds, a single audio LLM can replace a collection of task-specific deep networks for heart murmur phenotyping, simplifying automated auscultation analysis.
  • The demonstrated classification of long-tail diastolic features, which previous systems could not label, would give clinicians a machine-readable description of diastolic murmurs that automated tools currently lack.
  • The segmentation front-end's improvement in zero-shot transfer suggests that preprocessing periodic biomedical audio into meaningful segments is a broadly useful step for audio LLMs, not just for heart sounds.
  • The joint modeling of 11 features implies that the LLM captures shared acoustic structure across murmur traits, allowing one system to produce a complete murmur description rather than separate binary calls.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct head-to-head rerun of the two baseline systems on the exact test split used here would settle whether the 8-of-11 claim is robust, because the tables compare against numbers reported under different evaluation protocols.
  • The poor grading results and the paper's own explanation point to a testable fix: unfreezing the text encoder or replacing Roman-numeral labels with numeric ones may recover grading accuracy, and if so the same model could plausibly reach all 11 features.
  • The segmentation front-end's success on heart sounds suggests a transferable recipe for other periodic biomedical sounds, such as respiratory or bowel sounds, where segmenting events before an LLM could improve few-shot classification.
  • If audio LLMs can reliably describe murmur features, the next natural step—implicit in the paper but not tested—is to have the same model generate a free-text clinical impression from the murmur description, making it an assistant rather than a labeler.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a PCG-analysis system that combines a SSAMBA-based segmentation front-end with a LoRA-finetuned Qwen2-Audio audio-LLM, and evaluates it on 11 expert-labeled murmur features (systolic and diastolic timing, shape, grading, pitch, quality, plus weighted murmur accuracy) from the PhysioNet CirCor DigiScope dataset. Results are reported on a 75/25 patient-level split, with comparisons to Deep CardioSound and M2D+AST and a zero-shot normal/abnormal evaluation on PhysioNet 2016 and Pascal datasets. The abstract and introduction claim that the LLM-based model outperforms state-of-the-art methods in 8 of 11 features and performs comparably in the remaining 3.

Significance. The question is timely: adapting audio-LLMs to phonocardiogram analysis, and especially to non-binary murmur features, is a useful direction and the diastolic features are genuinely under-studied. The paper is empirical only; it provides no code, no release of prompts or hyperparameters, and no machine-checked artifacts. If the reported comparisons were valid, the result would be of interest, but the headline claim is contradicted by the paper's own tables and the evaluation lacks basic statistical safeguards, so the positive significance is not established in the current form.

major comments (4)
  1. [Abstract; §1; Tables 1–2] The claim that the model 'outperforms state-of-the-art methods in 8 of the 11 features and performs comparably in the remaining 3' is contradicted by the reported numbers. Table 1 shows that for systolic features this work W.S achieves 100/100/33.4/100/100 versus Deep CardioSound's 96.6/96.3/96.6/96.6/96.5, so only timing, shape, pitch, and quality outperform, while grading is 33.4% versus 96.6%. Table 2 shows murmur W.acc of 75.6% versus 83.2% for M2D+AST, also worse. The five diastolic features have no baseline entries. No subset of the tables yields 8 outperforming and 3 comparable features; the 'remaining 3' is never identified. This internal inconsistency must be corrected or the claim removed.
  2. [§4; Tables 1–2] The evaluation reports only point estimates of accuracy with no error bars, confidence intervals, statistical tests, or majority-class baselines. Given the small test subsets for long-tail diastolic features and the 33.4% systolic grading result, it is impossible to tell whether differences such as 100% versus 99.7% are meaningful or whether grading performance is at or below chance. The statement in §4 that segmentation 'does improve diastolic grading performance above chance level' is unsupported because no chance baseline is provided.
  3. [§3.3; Tables 1–2] The comparisons with Deep CardioSound [12] and M2D+AST [28] are not established as protocol-comparable. The paper uses a custom 75/25 patient-level split of CirCor, whereas the cited W.acc definition in [36] and the M2D+AST result in [28] are tied to the PhysioNet Challenge 2022 evaluation setup, and Deep CardioSound [12] was evaluated under its own data split. Without re-running the baselines under identical splits, feature label sets, and accuracy definitions, the claimed outperformance on the four systolic features is unsupported.
  4. [§4; Table 2] The absence of any previous results for the five diastolic features does not establish that this model succeeds on them; it only means no comparison was made. The text says these are 'long-tail' features and that previous methods 'failed to classify' them, but no class-frequency statistics or prior negative results are cited. The paper should either provide a baseline trained on the same features or explicitly frame these numbers as first unreplicated measurements rather than as evidence of superiority.
minor comments (6)
  1. [§3.3] The text says the data are resampled to '16,000 kHz'; this should be 16 kHz.
  2. [§3.1.2] LoRA is low-rank adaptation, not 'full low-rank approximation'; also 'matrics' should be 'matrices'.
  3. [§1] There is a typo: 'repeatative' should be 'repetitive'.
  4. [Table 2 heading] The heading says 'weighed murmur classification'; this should be 'weighted murmur classification'.
  5. [§3.1.1] The description of SSAMBA finetuning says the model is trained to segment 'occurrences and silences', but it is unclear what labels are used and whether they come from the CirCor heartbeat annotations or are produced by another method; this should be specified for reproducibility.
  6. [§3.1.2 and §3.3] The paper does not report LoRA rank and alpha, learning rate, number of epochs, prompt templates, or the three question phrasing variants, which makes the experiment difficult to reproduce.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's results come from held-out test evaluation and no prediction reduces to a fitted parameter or self-cited theorem.

full rationale

The paper contains no derivation chain that could be circular. The central claims are empirical: Qwen2-Audio is finetuned on PhysioNet CirCor training labels and evaluated on a 25% held-out test split, and the only use of an author-affiliated model is SSAMBA as a segmentation front-end. The SSAMBA component is itself finetuned on PCG segmentation labels from a subset of the training data; the 11 feature classification results are not defined in terms of SSAMBA's outputs, and the N.S. (no-segmentation) results independently show the same feature-level pattern. The comparisons to Deep CardioSound [12] and M2D+AST [28] quote externally reported numbers and are not constructed from the present model's fitted values. The abstract's '8 of 11' claim is inconsistent with the paper's own Tables 1 and 2, but that is an internal correctness/consistency problem, not a circularity, because no equation or fitted parameter is reused as its own input.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central comparison rests on the reliability of expert-annotated labels, the comparability of cited baseline numbers, and the validity of the multiple-choice formulation. No code or error bars are provided to verify these assumptions.

free parameters (2)
  • LoRA rank and alpha
    Hyperparameters for Qwen2-Audio finetuning are not reported in the paper; they affect classification accuracy and cannot be reproduced.
  • Segmentation finetuning subset size = one-third of training set
    The SSAMBA segmentation head is trained on only one-third of the training data; this choice is not justified and may affect robustness results.
assumptions (3)
  • domain assumption PhysioNet CirCor expert annotations are treated as ground truth for all 11 murmur features.
    All labels come from the dataset's semi-supervised expert annotation process; errors in labels would propagate to evaluation.
  • domain assumption The multiple-choice format with randomized phrasing and padded distractors does not introduce systematic bias.
    The paper pads options with repeated incorrect answers and randomizes question phrasing, but does not validate that this does not leak the correct answer.
  • domain assumption Segmentation labels used to finetune SSAMBA are accurate enough for preprocessing.
    The segmentation front-end relies on dataset heartbeat timing labels; incorrect segmentation could distort audio before classification.

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Cite this review

Pith. "Pith review of Exploring Finetuned Audio-LLM on Heart Murmur Features." pith.science (2026). https://pith.science/paper/WSOP5PYU

@misc{pith2026250113884,
  author       = {Pith},
  title        = {Pith review of: Exploring Finetuned Audio-LLM on Heart Murmur Features},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WSOP5PYU}},
  note         = {Machine review of arXiv:2501.13884}
}
read the original abstract

Large language models (LLMs) for audio have excelled in recognizing and analyzing human speech, music, and environmental sounds. However, their potential for understanding other types of sounds, particularly biomedical sounds, remains largely underexplored despite significant scientific interest. In this study, we focus on diagnosing cardiovascular diseases using phonocardiograms, i.e., heart sounds. Most existing deep neural network (DNN) paradigms are restricted to heart murmur classification (healthy vs unhealthy) and do not predict other acoustic features of the murmur such as timing, grading, harshness, pitch, and quality, which are important in helping physicians diagnose the underlying heart conditions. We propose to finetune an audio LLM, Qwen2-Audio, on the PhysioNet CirCor DigiScope phonocardiogram (PCG) dataset and evaluate its performance in classifying 11 expert-labeled murmur features. Additionally, we aim to achieve more noise-robust and generalizable system by exploring a preprocessing segmentation algorithm using an audio representation model, SSAMBA. Our results indicate that the LLM-based model outperforms state-of-the-art methods in 8 of the 11 features and performs comparably in the remaining 3. Moreover, the LLM successfully classifies long-tail murmur features with limited training data, a task that all previous methods have failed to classify. These findings underscore the potential of audio LLMs as assistants to human cardiologists in enhancing heart disease diagnosis.

Figures

Figures reproduced from arXiv: 2501.13884 by the authors.

Figure 1
Figure 1. Proposed PCG system incorporating a finetuned audio LLM Qwen2-Audio model with a front-end segmentation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

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