# SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation

arXiv: https://arxiv.org/abs/2510.10719 · HTML: https://mlsanity.com/paper/2510.10719/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [SSAST: Self-Supervised Audio Spectrogram Transformer](https://mlsanity.com/paper/2110.09784/) (2021)
- [Self-Supervised Representation Learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2110.09327/) (2021)
- [MAE-AST: Masked Autoencoding Audio Spectrogram Transformer](https://mlsanity.com/paper/2203.16691/) (2022)
- [Episode-based Prototype Generating Network for Zero-Shot Learning](https://mlsanity.com/paper/1909.03360/) (2019)
- [Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning](https://mlsanity.com/paper/2102.06605/) (2021)
- [Multi-Format Contrastive Learning of Audio Representations](https://mlsanity.com/paper/2103.06508/) (2021)
- [CLAR: Contrastive Learning of Auditory Representations](https://mlsanity.com/paper/2010.09542/) (2020)
- [Short-segment heart sound classification using an ensemble of deep convolutional neural networks](https://mlsanity.com/paper/1810.11573/) (2018)
- [Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection](https://mlsanity.com/paper/2305.16691/) (2023)
- [Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds](https://mlsanity.com/paper/2208.14845/) (2022)
- [Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning](https://mlsanity.com/paper/2502.12031/) (2025)
- [Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN](https://mlsanity.com/paper/2303.11423/) (2023)
- [Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning](https://mlsanity.com/paper/2312.00502/) (2023)

## Abstract (arXiv)

The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-annotated data. In this paper, we propose the Self-Supervised Dual-Path Prototypical Network (SS-DPPN), a foundation model for cardiac audio representation and classification from unlabeled data. The framework introduces a dual-path contrastive learning based architecture that simultaneously processes 1D waveforms and 2D spectrograms using a novel hybrid loss. For the downstream task, a metric-learning approach using a Prototypical Network was used that enhances sensitivity and produces well-calibrated and trustworthy predictions. SS-DPPN achieves state-of-the-art performance on four cardiac audio benchmarks. The framework demonstrates exceptional data efficiency with a fully supervised model on three-fold reduction in labeled data. Finally, the learned representations generalize successfully across lung sound classification and heart rate estimation. Our experiments and findings validate SS-DPPN as a robust, reliable, and scalable foundation model for physiological signals.