DEEPCHORUS：一种用于副歌检测的多尺度卷积与自注意力混合模型
DEEPCHORUS: A Hybrid Model of Multi-scale Convolution and Self-attention for Chorus Detection
AI总结：
本文提出端到端混合模型 DeepChorus，结合多尺度网络与自注意力卷积网络检测副歌，并用自适应阈值生成结果，在多数情况下优于现有方法。
摘要：
副歌检测是音乐信号处理中的一个具有挑战性的问题，因为在流行歌曲中副歌通常会重复多次，并且往往具有丰富的乐器编排和复杂的节奏形式。现有大多数研究基于响度和出现频率等显式特征，关注副歌段落的可接受性。这些关于副歌的预先假设限制了相关方法的泛化能力，导致其对主歌等其他重复段落产生误检。为解决该问题，本文提出端到端的副歌检测模型 DeepChorus，以减少工程工作量和对先验知识的需求。所提模型包含两个主要结构：i）多尺度网络，用于获得副歌片段的初步表示；ii）自注意力卷积网络，用于进一步处理特征，生成表示副歌存在情况的概率曲线。为获得最终结果，本文采用自适应阈值对原始曲线进行二值化。实验结果表明，DeepChorus 在大多数情况下优于现有最先进方法。
英文摘要：
Chorus detection is a challenging problem in musical signal processing as the chorus often repeats more than once in popular songs, usually with rich instruments and complex rhythm forms. Most of the existing works focus on the receptiveness of chorus sections based on some explicit features such as loudness and occurrence frequency. These pre-assumptions for chorus limit the generalization capacity of these methods, causing misdetection on other repeated sections such as verse. To solve the problem, in this paper we propose an end-to-end chorus detection model DeepChorus, reducing the engineering effort and the need for prior knowledge. The proposed model includes two main structures: i) a Multi-Scale Network to derive preliminary representations of chorus segments, and ii) a Self-Attention Convolution Network to further process the features into probability curves representing chorus presence. To obtain the final results, we apply an adaptive threshold to binarize the original curve. The experimental results show that DeepChorus outperforms existing state-of-the-art methods in most cases.
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