从失谐到和谐：基于分解协和性的训练方法用于改进音频和弦估计
From Discord to Harmony: Decomposed Consonance-based Training for Improved Audio Chord Estimation
AI总结：
针对音频和弦估计中存在的标注主观性、类别不平衡等性能瓶颈问题，该研究提出协和性感知距离度量及融合协和性标签平滑的分解式conformer模型，有效提升了和弦估计的音乐合理性与性能。
摘要：
音频和弦估计（Audio Chord Estimation, ACE）在音乐信息研究中占据核心地位，因其与音乐转录和分析的相关性，二十余年来一直受到学界关注。尽管该领域已取得显著进展，但任务仍存在诸多挑战，尤其是和声内容的独特特性导致现有系统的性能遭遇瓶颈。这些挑战包括标注者主观性——不同标注者的解读差异会造成标注不一致，以及和弦数据集内的类别不平衡——部分和弦类别的样本占比远高于其他类别，给模型训练和评估带来困难。作为第一项贡献，本文使用超越传统二值度量的指标，对和弦标注中的标注者间一致性进行了评估。此外，我们提出了一种融合协和性信息的距离度量，可反映和声标注之间的感知相似性。分析表明，基于协和性的距离度量能更有效地捕捉标注间具有音乐意义的一致性。基于这些发现，我们提出了一种新型的基于conformer的ACE模型，通过基于协和性的标签平滑将协和性概念融入模型中。该模型还通过分别估计根音、低音和所有音符激活度来解决类别不平衡问题，从而能够从分解输出中重建和弦标签。
英文摘要：
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.
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。
