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Benchmarking Representations for Speech, Music, and Acoustic Events
May 3, 2024, 4:53 a.m. | Moreno La Quatra, Alkis Koudounas, Lorenzo Vaiani, Elena Baralis, Luca Cagliero, Paolo Garza, Sabato Marco Siniscalchi
cs.LG updates on arXiv.org arxiv.org
Abstract: Limited diversity in standardized benchmarks for evaluating audio representation learning (ARL) methods may hinder systematic comparison of current methods' capabilities. We present ARCH, a comprehensive benchmark for evaluating ARL methods on diverse audio classification domains, covering acoustic events, music, and speech. ARCH comprises 12 datasets, that allow us to thoroughly assess pre-trained SSL models of different sizes. ARCH streamlines benchmarking of ARL techniques through its unified access to a wide range of domains and its …
abstract arch arxiv audio benchmark benchmarking benchmarks capabilities classification comparison cs.lg cs.sd current datasets diverse diversity domains eess.as events hinder music representation representation learning speech type
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