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https://www.selleckchem.com/products/raphin1.html These models also produced accurate annoyance predictions. BSEL was the best single-metric predictor, with ESEL close behind. Including Heaviness, Duration, and rate of change of Loudness resulted in R2 values as high as 0.90.This article presents a polyphonic pitch tracking system that is able to extract both framewise and note-based estimates from audio. The system uses several artificial neural networks trained individually in a deep layered learning setup. First, cascading networks are applied to a spectrogram for framewise fundamental frequency (f0) estimation. A sparse receptive field is learned by the first network and then used as a filter kernel for parameter sharing throughout the system. The f0 activations are connected across time to extract pitch contours. These contours define a framework within which subsequent networks perform onset and offset detection, operating across both time and smaller pitch fluctuations at the same time. As input, the networks use, e.g., variations of latent representations from the f0 estimation network. Finally, erroneous tentative notes are removed one by one in an iterative procedure that allows a network to classify notes within a correct context. The system was evaluated on four public test sets MAPS, Bach10, TRIOS, and the MIREX Woodwind quintet and achieved state-of-the-art results for all four datasets. It performs well across all subtasks f0, pitched onset, and pitched offset tracking.Allophonic patterns of variation in English laterals have been well studied in phonetics and phonology for decades, but establishing broad generalizations across varieties has proven challenging. In this study, a typology of onset/coda lateral distinctions in English is advanced using crowdsourced recordings from 95 speakers across twelve dialects of Anglo (UK) English. Results confirm the existence of dialects with and without onset/coda distinctions, and conditional inference trees ar

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