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This paper proposes SRT (Super-Resolution for Time Series), a framework that reconstructs high-resolution temporal patterns from low-resolution inputs using a disentangled rectified flow approach. The method decomposes input into trend and seasonal components, applies implicit neural representation for resolution alignment, and introduces cross-resolution attention to generate fine-grained details, achieving state-of-the-art performance on multiple datasets.
MERIT is a framework that learns disentangled music representations for melody, rhythm, and timbre using conditional audio generation and source-separated stems, enabling nuanced and factor-specific audio similarity queries.