MuSLCAT：多尺度多级卷积注意力Transformer用于原始波形上的区分性音乐建模
MuSLCAT: Multi-Scale Multi-Level Convolutional Attention Transformer for Discriminative Music Modeling on Raw Waveforms
摘要：
在这项工作中，我们的目标是通过在一个有效的端到端架构中建模顺序（时间）和层次信息来提高基于波形的歧视性音乐网络的表达能力。我们提出了MuSLCAT，或多尺度和多级卷积注意力Transformer，一种新的架构，用于直接从原始波形记录中学习复杂音乐标签的鲁棒表示。我们还介绍了MuSLCAT的一个轻量级变体MuSLCAN，即Multi-scale and Multi-level Convolutional Attention Network的缩写。MuSLCAT和MuSLCAN都通过集成前端-后端架构从多个尺度和级别建模功能。前端针对不同的频率范围，同时通过使用两个具有注意力增强卷积（AAC）块的卷积注意力网络来建模远程依赖性和多级交互。后端动态地重新校准多尺度和水平的功能，从前端提取的结合自我注意。MuSLCAT和MuSLCAN之间的区别在于它们的后端组件。MuSLCAT的后端是BERT的修改版本。而MuSLCAN是一个简单的AAC块。我们验证了建议MuSLCAT和MuSLCAN架构进行比较，以最先进的网络上的四个基准数据集的音乐标记和流派识别。我们的实验表明，MuSLCAT和MuSLCAN始终产生竞争力的结果相比，国家的最先进的波形为基础的模型，但需要少得多的参数。
英文摘要：
In this work, we aim to improve the expressive capacity of waveform-based discriminative music networks by modeling both sequential (temporal) and hierarchical information in an efficient end-to-end architecture. We present MuSLCAT, or Multi-scale and Multi-level Convolutional Attention Transformer, a novel architecture for learning robust representations of complex music tags directly from raw waveform recordings. We also introduce a lightweight variant of MuSLCAT called MuSLCAN, short for Multi-scale and Multi-level Convolutional Attention Network. Both MuSLCAT and MuSLCAN model features from multiple scales and levels by integrating a frontend-backend architecture. The frontend targets different frequency ranges while modeling long-range dependencies and multi-level interactions by using two convolutional attention networks with attention-augmented convolution (AAC) blocks. The backend dynamically recalibrates multi-scale and level features extracted from the frontend by incorporating self-attention. The difference between MuSLCAT and MuSLCAN is their backend components. MuSLCAT's backend is a modified version of BERT. While MuSLCAN's is a simple AAC block. We validate the proposed MuSLCAT and MuSLCAN architectures by comparing them to state-of-the-art networks on four benchmark datasets for music tagging and genre recognition. Our experiments show that MuSLCAT and MuSLCAN consistently yield competitive results when compared to state-of-the-art waveform-based models yet require considerably fewer parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
