AI 中文总结
研究探索DTW可并行化替代方法，介绍分段DTW算法变体，其将全局成本矩阵分解为子矩阵计算，在音频对齐任务中评估性能，结果接近常规DTW，且几乎所有计算可并行化，一个变体表现更优。
AI 中文摘要
在这项工作中，我们探索了用于全局对齐两个特征序列的动态时间规整（DTW）的可并行化替代方法。DTW的一个主要实际限制是其二次计算和内存成本。先前的工作试图以各种方式降低计算成本，例如在成本矩阵中设置带或使用多分辨率方法。在这项工作中，我们利用计算资源丰富这一事实，转而专注于探索用可并行化算法来近似本质上顺序执行的DTW算法。我们描述了一种名为分段DTW算法的两种变体，其中全局成本矩阵被分解为较小的子矩阵，对每个子矩阵执行子序列DTW，并使用结果来解决一个段级动态规划问题，该问题指定全局最优对齐路径。我们使用肖邦玛祖卡数据集在音频 - 音频对齐任务上评估了所提出的对齐算法，结果表明它们与常规DTW的性能非常接近。我们进一步证明，分段DTW中的几乎所有计算都是可并行化的，并且出于经验和理论原因，其中一个变体在各方面都优于另一个。
英文摘要
In this work we explore parallelizable alternatives to DTW for globally aligning two feature sequences. One of the main practical limitations of DTW is its quadratic computation and memory cost. Previous works have sought to reduce the computational cost in various ways, such as imposing bands in the cost matrix or using a multiresolution approach. In this work, we utilize the fact that computation is an abundant resource and focus instead on exploring alternatives that approximate the inherently sequential DTW algorithm with one that is parallelizable. We describe two variations of an algorithm called Segmental DTW, in which the global cost matrix is broken into smaller sub-matrices, subsequence DTW is performed on each sub-matrix, and the results are used to solve a segment-level dynamic programming problem that specifies a globally optimal alignment path. We evaluate the proposed alignment algorithms on an audio-audio alignment task using the Chopin Mazurka dataset, and we show that they closely match the performance of regular DTW. We further demonstrate that almost all of the computations in Segmental DTW are parallelizable, and that one of the variants is unilaterally better than the other for both empirical and theoretical reasons.
CommentsPublished at ICASSP 2021
Journal refProc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 106-110
DOI：10.1109/ICASSP39728.2021.9413827
