Exact, Parallelizable Dynamic Time Warping Alignment with Linear Memory
This provides a solution for audio alignment in music information retrieval, allowing exact DTW with reduced memory, though it is incremental as it trades memory for computation.
The authors tackled the problem of exact dynamic time warping (DTW) alignment for audio, which traditionally requires quadratic memory, by developing a divide-and-conquer algorithm that uses linear memory (O(M+N)) and is parallelizable, enabling exact alignments on orchestral music to benchmark approximate methods at new scales.
Audio alignment is a fundamental preprocessing step in many MIR pipelines. For two audio clips with M and N frames, respectively, the most popular approach, dynamic time warping (DTW), has O(MN) requirements in both memory and computation, which is prohibitive for frame-level alignments at reasonable rates. To address this, a variety of memory efficient algorithms exist to approximate the optimal alignment under the DTW cost. To our knowledge, however, no exact algorithms exist that are guaranteed to break the quadratic memory barrier. In this work, we present a divide and conquer algorithm that computes the exact globally optimal DTW alignment using O(M+N) memory. Its runtime is still O(MN), trading off memory for a 2x increase in computation. However, the algorithm can be parallelized up to a factor of min(M, N) with the same memory constraints, so it can still run more efficiently than the textbook version with an adequate GPU. We use our algorithm to compute exact alignments on a collection of orchestral music, which we use as ground truth to benchmark the alignment accuracy of several popular approximate alignment schemes at scales that were not previously possible.