用卷积网络加动态规划实现精准哼唱转录，提升标注质量。
Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming
- 结合卷积神经网络与动态规划后处理，优化哼唱转录流程。
- 在新标注数据集上达到当前最优（SOTA）转录准确率。
- 修复原始数据标注缺陷，开源代码与修正数据集供研究使用。
我们提出一种新颖的哼唱转录方法，结合基于卷积神经网络（CNN）的架构与基于动态规划的后处理算法，利用近期发布的HumTrans数据集。针对该数据集中存在偏移（offset）和起始（onset）标注不准确的问题，我们提出改进启发式方法，生成更精确的标注，构建了高质量的标注数据集，有助于未来研究。同时，我们将所提方法与其他方法进行对比，证明其在转录准确性上达到当前最优（SOTA）水平。所有代码及修正后的数据集已公开于 https://github.com/shubham-gupta-30/humming_transcription。
原文摘要 · Abstract (English)
We propose a novel approach for humming transcription that combines a CNN-based architecture with a dynamic programming-based post-processing algorithm, utilizing the recently introduced HumTrans dataset. We identify and address inherent problems with the offset and onset ground truth provided by the dataset, offering heuristics to improve these annotations, resulting in a dataset with precise annotations that will aid future research. Additionally, we compare the transcription accuracy of our method against several others, demonstrating state-of-the-art (SOTA) results. All our code and corrected dataset is available at https://github.com/shubham-gupta-30/humming_transcription
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