Self-supervised Auxiliary Loss for Metric Learning in Music
Similarity-based Retrieval and Auto-tagging
- URL: http://arxiv.org/abs/2304.07449v1
- Date: Sat, 15 Apr 2023 02:00:28 GMT
- Title: Self-supervised Auxiliary Loss for Metric Learning in Music
Similarity-based Retrieval and Auto-tagging
- Authors: Taketo Akama, Hiroaki Kitano, Katsuhiro Takematsu, Yasushi Miyajima,
and Natalia Polouliakh
- Abstract summary: We propose a model that builds on the self-supervised learning approach to address the similarity-based retrieval challenge.
We also found that refraining from employing augmentation during the fine-tuning phase yields better results.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the realm of music information retrieval, similarity-based retrieval and
auto-tagging serve as essential components. Given the limitations and
non-scalability of human supervision signals, it becomes crucial for models to
learn from alternative sources to enhance their performance. Self-supervised
learning, which exclusively relies on learning signals derived from music audio
data, has demonstrated its efficacy in the context of auto-tagging. In this
study, we propose a model that builds on the self-supervised learning approach
to address the similarity-based retrieval challenge by introducing our method
of metric learning with a self-supervised auxiliary loss. Furthermore,
diverging from conventional self-supervised learning methodologies, we
discovered the advantages of concurrently training the model with both
self-supervision and supervision signals, without freezing pre-trained models.
We also found that refraining from employing augmentation during the
fine-tuning phase yields better results. Our experimental results confirm that
the proposed methodology enhances retrieval and tagging performance metrics in
two distinct scenarios: one where human-annotated tags are consistently
available for all music tracks, and another where such tags are accessible only
for a subset of tracks.
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