Benchmarking Representations for Speech, Music, and Acoustic Events

• URL: http://arxiv.org/abs/2405.00934v1
• Date: Thu, 2 May 2024 01:24:53 GMT
• Title: Benchmarking Representations for Speech, Music, and Acoustic Events
• Authors: Moreno La Quatra, Alkis Koudounas, Lorenzo Vaiani, Elena Baralis, Luca Cagliero, Paolo Garza, Sabato Marco Siniscalchi,
• Abstract summary: ARCH is a comprehensive benchmark for evaluating ARL methods on diverse audio classification domains. ARCH comprises 12 datasets, that allow us to thoroughly assess pre-trained SSL models of different sizes. To address the current lack of open-source, pre-trained models for non-speech audio, we also release new pre-trained models that demonstrate strong performance on non-speech datasets.
• Score: 24.92641211471113
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 ability to readily incorporate new datasets and models. To address the current lack of open-source, pre-trained models for non-speech audio, we also release new pre-trained models that demonstrate strong performance on non-speech datasets. We argue that the presented wide-ranging evaluation provides valuable insights into state-of-the-art ARL methods, and is useful to pinpoint promising research directions.

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