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Abstract: Heart auscultation remains a valuable approach for early detection of valvular heart disease, but its efficacy depends on physician skill. The rise of telehealth services necessitates consideration of heart sound transmission and classification through compressed formats. While neural audio codecs offer impressive compression rates, they may introduce distortions that could impact classification accuracy. We propose a novel approach to perform heart sound classification directly on the continuous representations of the codec, rather than on reconstructed audio. Our method demonstrates good performance on the heart sound classification task while leveraging the encoder of the audio codec, potentially improving robustness and efficiency in telehealth applications. Using the Descript Audio Codec encoder combined with a modified M5 convolutional neural network, our approach achieves a Classification Error Rate of 0.78 on the Yaseen Dataset, outperforming models trained on transmitted audio and comparing favorably with state-of-the-art methods. This performance, coupled with the method's inherent robustness to transmission artifacts, highlights its potential for improving remote cardiac diagnostics in telehealth settings.