论文标题
音乐仪器分类重编程
Music Instrument Classification Reprogrammed
论文作者
论文摘要
音乐仪器分类方法的性能是音乐信息检索中的一项流行任务，通常受到缺乏注释数据培训的可用性的影响和限制。我们建议通过“重编程”来解决此问题，该技术利用了预先训练的深度和复杂的神经网络，最初通过修改和映射预训练模型的输入和输出来定位不同的任务。我们证明，重编程可以有效利用针对不同任务所学的表示的能力，并且所得的重编程系统可以在训练参数的一小部分上在PAR甚至超过最先进的系统上执行。因此，我们的结果表明，重编程是一种有前途的技术，可能适用于数据稀缺阻碍的其他任务。
The performance of approaches to Music Instrument Classification, a popular task in Music Information Retrieval, is often impacted and limited by the lack of availability of annotated data for training. We propose to address this issue with "reprogramming," a technique that utilizes pre-trained deep and complex neural networks originally targeting a different task by modifying and mapping both the input and output of the pre-trained model. We demonstrate that reprogramming can effectively leverage the power of the representation learned for a different task and that the resulting reprogrammed system can perform on par or even outperform state-of-the-art systems at a fraction of training parameters. Our results, therefore, indicate that reprogramming is a promising technique potentially applicable to other tasks impeded by data scarcity.
