The Maskogram: A Tool to Illustrate Zones of Masking
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The prediction of masking in marine mammals is most commonly based on the power spectrum model of masking and the concept of equal power of signal and noise at the detection threshold, within the auditory filter centred at the peak of the signal. While this model works well for narrow-band signals embedded in broadband noise, it fails in many realistic listening scenarios. In this paper, a visualisation tool, called a maskogram, is presented that illustrates the extent of the zone of masking around a noise source and with which the effects of various parameters and anti-masking mechanisms can be examined. A series of maskograms is presented based on behavioural experiments with a beluga whale (Delphinapterus leucas) for which the signal was a recorded beluga call and the noise was recorded from an icebreaker. Certain masking release mechanisms, such as comodulation masking release, within-valley listening, and multiple looks, likely occurred during the behavioural experiments and are indirectly included in the data feeding into the maskograms. The effects of a spatial release from masking are illustrated based on data from other species, signals, and noise. Studies with realistic signals and noise are needed to show the limitations of the existing models, to determine masking in real-world situations, to better understand masking release mechanisms, and to ultimately improve models of masking.
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From spin noise to systematics: Stochastic processes in the first International Pulsar Timing Array data releaseLentati, L.; Shannon, Ryan; Coles, W.; Verbiest, J.; van Haasteren, R.; Ellis, J.; Caballero, R.; Manchester, R.; Arzoumanian, Z.; Babak, S.; Bassa, C.; Bhat, N.; Brem, P.; Burgay, M.; Burke-Spolaor, S.; Champion, D.; Chatterjee, S.; Cognard, I.; Cordes, J.; Dai, S.; Demorest, P.; Desvignes, G.; Dolch, T.; Ferdman, R.; Fonseca, E.; Gair, J.; Gonzalez, M.; Graikou, E.; Guillemot, L.; Hessels, J.; Hobbs, G.; Janssen, G.; Jones, G.; Karuppusamy, R.; Keith, M.; Kerr, M.; Kramer, M.; Lam, M.; Lasky, P.; Lassus, A.; Lazarus, P.; Lazio, T.; Lee, K.; Levin, L.; Liu, K.; Lynch, R.; Madison, D.; McKee, J.; McLaughlin, M.; McWilliams, S.; Mingarelli, C.; Nice, D.; Oslowski, S.; Pennucci, T.; Perera, B.; Perrodin, D.; Petiteau, A.; Possenti, A.; Ransom, S.; Reardon, D.; Rosado, P.; Sanidas, S.; Sesana, A.; Shaifullah, G.; Siemens, X.; Smits, R.; Stairs, I.; Stappers, B.; Stinebring, D.; Stovall, K.; Swiggum, J.; Taylor, S.; Theureau, G.; Tiburzi, C.; Toomey, L.; Vallisneri, M.; van Straten, W.; Vecchio, A.; Wang, J.; Wang, Y.; You, X.; Zhu, W.; Zhu, X. (2016)We analyse the stochastic properties of the 49 pulsars that comprise the first International Pulsar Timing Array (IPTA) data release. We use Bayesian methodology, performing model selection to determine the optimal ...