用多轮非负矩阵分解解析DJ混音中的源曲片段
DJ Mix Transcription with Multi-Pass Non-Negative Matrix Factorization
- 通过多轮非负矩阵分解，将混音拆解为原始音频片段
- 相比动态时间对齐基线方法，识别准确率显著提升
- 适合音乐信号处理与音频逆向工程研究者
DJ混音转录是实现DJ混音逆向工程的关键步骤，旨在估计生成表演性混音所使用的源曲参数与音频效果。本文提出一种基于多轮非负矩阵分解（NMF）的新方法，其中字典矩阵对应混音中实际存在的源曲频谱切片。多轮策略旨在缓解大字典规模带来的高计算开销问题。所提方法通过跨轮次过滤增强时序连续性与稀疏性，在公开数据集上进行了评估。与基于动态时间对齐（DTW）的基线方法相比，结果表现良好，为未来基于NMF的音频转录应用铺平了道路。
原文摘要 · Abstract (English)
DJ mix transcription is a crucial step towards DJ mix reverse engineering, which estimates the set of parameters and audio effects applied to a set of existing tracks to produce a performative DJ mix. We introduce a new approach based on a multi-pass NMF algorithm where the dictionary matrix corresponds to a set of spectrogram slices of the source tracks present in the mix. The multi-pass strategy is motivated by the high computational cost resulting from the use of a large NMF dictionary. The proposed method uses inter-pass filtering to favor temporal continuity and sparseness and is evaluated on a publicly available dataset. Our comparative results considering a baseline method based on dynamic time warping (DTW) are promising and pave the way of future NMF-based applications.
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