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The current paradigm of exclusive spectrum assignment and allocation is creating artificial spectrum scarcity that has a dramatic impact on network performance and user experience. Thus, governments, industry and academia have endeavored to create novel spectrum management mechanisms that allow multi-tiered access. A key component of such an approach is deep understanding of spectrum utilization in time, frequency and space. To address this challenge, we propose AirVIEW, a one-pass, unsupervised spectrum characterization approach for rapid transmitter detection with high tolerance to noise. AirVIEW autonomously learns its parameters and employs wavelet decomposition in order to amplify and reliably detect transmissions at a given time instant. We show that AirVIEW can robustly identify transmitters even when their power is only 5dBm above the noise floor. Furthermore, we demonstrate AirVIEW’s ability to inform next-generation Dynamic Spectrum Access by characterizing essential transmitter properties in wideband spectrum measurements from 50MHz to 4.4GHz.