Low-end earphones are widely spread due to their affordability, but their limited speaker hardware often leads to poor music playback quality. This raises a key question: Can we compensate for hardware limitations to enhance the listening experience without modifying the device? Existing EQ-based approaches attempt this, but they rely on frequency response curves (FRCs) measured under ideal conditions, which fail to capture real-world distortions such as harmonic and intermodulation effects.

Electrocardiography (ECG) offers critical cardiovascular insights, such as identifying arrhythmias and myocardial ischemia, but enabling automated systems to answer complex clinical questions directly from ECG signals (ECG-QA) remains a significant challenge. Current approaches often lack robust multimodal reasoning capabilities or rely on generic architectures ill-suited for the nuances of physiological signals. We introduce Q-HEART, a novel multimodal framework designed to bridge this gap. Q-HEART leverages a powerful, adapted ECG encoder and integrates its representations with textual information via a specialized ECG-aware transformer-based mapping layer. Furthermore, Q-HEART leverages dynamic prompting and retrieval of relevant historical clinical reports to guide tuning the language model toward knowledge-aware ECG reasoning. Extensive evaluations on the benchmark ECG-QA dataset show Q-HEART achieves state-of-the-art performance, outperforming existing methods by a 4% improvement in exact match accuracy. Our work demonstrates the effectiveness of combining domain-specific architectural adaptations with knowledge-augmented LLM instruction tuning for complex physiological ECG analysis, paving the way for more capable and potentially interpretable clinical patient care systems. Our sample code and checkpoint are made available at https://github.com/manhph2211/Q-HEART.

The accurate interpretation of Electrocardiogram (ECG) signals is pivotal for diagnosing cardiovascular diseases. Integrating ECG signals with accompanying textual reports further holds immense potential to enhance clinical diagnostics by combining physiological data and qualitative insights. However, this integration faces significant challenges due to inherent modality disparities and the scarcity of labeled data for robust cross-modal learning. To address these obstacles, we propose D-BETA, a novel framework that pre-trains ECG and text data using a contrastive masked auto-encoder architecture, uniquely combining generative and boosted discriminative capabilities for robust cross-modal representations. This is accomplished through masked modality modeling, specialized loss functions, and an improved negative sampling strategy tailored for cross-modal alignment. Extensive experiments on five public datasets across diverse downstream tasks demonstrate that D-BETA significantly outperforms existing methods, achieving an average AUC improvement of 15% in linear probing with only one percent of training data and 2% in zero-shot performance without requiring training data over state-of-the-art models. These results highlight the effectiveness of D-BETA, underscoring its potential to advance automated clinical diagnostics through multi-modal representations.