AAI_2025_Capstone_Chronicles_Combined

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Our findings support the original hypothesis. Timbre corresponds to spectral and temporal patterns that machine learning models can learn to represent. Although the system does not yet accommodate all possible audio scenarios, it demonstrates that timbre retrieval can be performed at scale with both quantitative and perceptual validity. With continued refinement, the system has the potential to become a general-purpose engine for timbre search and discovery across any audio domain.

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