发表机构
Harvey Mudd College（哈维·穆德学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究长序列对齐的DTW并行化替代方法，提出四种算法，在音频对齐任务中表征性能，开发GPU实现，实验显示ParDTW最实用，能精确对齐且大幅减少长序列运行时间，还全面评估了算法的各项性能。
AI 中文摘要
本文研究了几种用于估计两个长序列对齐的动态时间规整（DTW）并行化替代方法。以往多数工作聚焦于降低DTW的总计算和/或内存成本，而本文重点是利用如GPU等针对并行处理优化的通用硬件来减少挂钟时间。提出并研究了四种不同的并行化对齐算法，其中三种通过将成对成本矩阵分解为矩形区域并行处理来计算DTW近似值，第四种通过沿对角线而非行或列处理成本矩阵来计算精确的DTW对齐。在音频-音频对齐任务中对算法性能进行了表征，并为两种性能最佳的算法开发了基于GPU的实现，即弱序分段DTW（WSDTW）和并行化对角DTW（ParDTW）。实验表明ParDTW是四种算法中最实用的，它能计算精确的DTW对齐，与当前替代方法相比，在长序列上运行时间减少1.5到2个数量级。还对所提算法的对齐精度、运行时间和实际局限性进行了全面评估和研究。
英文摘要
This article investigates several parallelizable alternatives to DTW for estimating the alignment between two long sequences. Whereas most previous work has focused on reducing the total computation and/or memory costs of DTW, our focus is instead on reducing wall clock time by utilizing common hardware like GPUs that are optimized for parallel processing. We propose and study four different parallelizable alignment algorithms: the first three algorithms compute approximations of DTW by breaking the pairwise cost matrix into rectangular regions and processing the regions in parallel, and the fourth algorithm computes an exact DTW alignment by processing the cost matrix along diagonals rather than rows or columns. We characterize the performance of our proposed alignment algorithms on an audio-audio alignment task, and we develop GPU-based implementations for the two best-performing algorithms, which we call weakly-ordered Segmental DTW (WSDTW) and Parallelized Diagonal DTW (ParDTW). Our experiments indicate that ParDTW is the most practical and useful of the four algorithms: it computes an exact DTW alignment and reduces runtime by 1.5 to 2 orders of magnitude on long sequences compared to current alternatives. We present a comprehensive evaluation and study of the alignment accuracy, runtime, and practical limitations of the proposed alignment algorithms.
CommentsPublished in IEEE/ACM Transactions on Audio, Speech, and Language Processing
Journal refIEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 2117-2127, 2022
DOI：10.1109/TASLP.2022.3180673
