Benchmarking Representations for Speech, Music, and Acoustic Events
<|reference_start|>Benchmarking Representations for Speech, Music, and Acoustic Events: 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.<|reference_end|>
@article{la quatra2024benchmarking,
    title={Benchmarking Representations for Speech, Music, and Acoustic Events},
    author={Moreno La Quatra, Alkis Koudounas, Lorenzo Vaiani, Elena Baralis, Luca
  Cagliero, Paolo Garza, Sabato Marco Siniscalchi},
    journal={arXiv preprint arXiv:2405.00934},
    year={2024},
    doi={10.1109/ICASSPW62465.2024.10625960},
    archivePrefix={arXiv},
    eprint={2405.00934},
    primaryClass={eess.AS cs.LG cs.SD}
}