Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming

• URL: http://arxiv.org/abs/2211.01317v3
• Date: Wed, 3 May 2023 04:22:54 GMT
• Title: Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
• Authors: Yun-Ning Hung, Chao-Han Huck Yang, Pin-Yu Chen, Alexander Lerch
• Abstract summary: We introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR) NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method.
• Score: 129.4950757742912
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset.

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