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Unsupervised Representation Learning For Context of Vocal Music
Unsupervised Representation Learning for Context of Vocal Music”, 21st International Society for Music Information Retrieval Conference, Montréal, Canada, 2020. Expand
A Model-Driven Exploration of Accent Within the Amateur Singing Voice
We seek to detect characteristics of regional language accent in solo singing using two variants of convolutional neural networks to classify reported country and language from ten countries during… Expand
Vocal Biomarker Assessment Following Pediatric Traumatic Brain Injury: A Retrospective Cohort Study
We apply a diverse set of acoustic, audio-based vocal biomarkers to study TBI-affected speech of a pediatric population over time. Expand
Self-Supervised Representation Learning for Vocal Music Context.
In music and speech, meaning is derived at multiple levels of context. Affect, for example, can be inferred both by a short sound token and by sonic patterns over a longer temporal window such as an… Expand
A cluster analysis of harmony in the McGill Billboard dataset
We set out to perform a cluster analysis of harmonic structures (specifically, chord-to-chord transitions) in the McGill Billboard dataset, to determine whether there is evidence of multiple harmonic grammars and practices in the corpus, and if so, what the optimal division of songs, according to those harmonic Grammars, is. Expand