标题： 从混乱到和谐：基于分解协和的音频和弦估计训练方法 显示英文标题

标题： From Discord to Harmony: Decomposed Consonance-based Training for Improved Audio Chord Estimation

摘要： 音频和弦估计（ACE）在音乐信息研究中起着关键作用，由于其与音乐转录和分析的相关性，已有二十多年的研究关注。 尽管取得了显著进展，该任务仍然存在挑战，特别是关于和声内容的独特特性，这导致现有系统的性能达到了一个瓶颈。 这些挑战包括标注者主观性，不同标注者之间的不同解释导致不一致，以及和弦数据集中的类别不平衡，某些和弦类别比其他类别更常出现，这给模型训练和评估带来了困难。 作为第一项贡献，本文提出了对和弦标注中标注者间一致性的评估，使用了超越传统二元度量的指标。 此外，我们提出了一种基于协和性的距离度量，反映了和声标注之间的感知相似性。 我们的分析表明，基于协和性的距离度量更能有效地捕捉标注之间的音乐上有意义的一致性。 在这些发现的基础上，我们引入了一种基于转换器的新型ACE模型，通过基于协和性的标签平滑将协和概念整合到模型中。 所提出的模型还通过分别估计根音、低音和所有音符激活来解决类别不平衡问题，从而能够从分解后的输出中重建和弦标签。 显示英文摘要

摘要： Audio Chord Estimation (ACE) holds a pivotal role in music information research, having garnered attention for over two decades due to its relevance for music transcription and analysis. Despite notable advancements, challenges persist in the task, particularly concerning unique characteristics of harmonic content, which have resulted in existing systems' performances reaching a glass ceiling. These challenges include annotator subjectivity, where varying interpretations among annotators lead to inconsistencies, and class imbalance within chord datasets, where certain chord classes are over-represented compared to others, posing difficulties in model training and evaluation. As a first contribution, this paper presents an evaluation of inter-annotator agreement in chord annotations, using metrics that extend beyond traditional binary measures. In addition, we propose a consonance-informed distance metric that reflects the perceptual similarity between harmonic annotations. Our analysis suggests that consonance-based distance metrics more effectively capture musically meaningful agreement between annotations. Expanding on these findings, we introduce a novel ACE conformer-based model that integrates consonance concepts into the model through consonance-based label smoothing. The proposed model also addresses class imbalance by separately estimating root, bass, and all note activations, enabling the reconstruction of chord labels from decomposed outputs.