Benchmarking dynamic time warping for music retrieval

We study the performance of three dynamic programming methods on music retrieval. The methods are designed for time series matching but can be directly applied to retrieval of music. Dynamic Time Warping (DTW) identifies an optimal alignment between two time series, and computes the matching cost corresponding to that alignment. Significant speed-ups can be achieved by constrained Dynamic Time Warping (cDTW), which narrows down the set of positions in one time series that can be matched with specific positions in the other time series. Both methods are designed for full sequence matching but can also be applied for subsequence matching, by using a sliding window over each database sequence to compute a matching score for each database subsequence. In addition, SPRING is a dynamic programming approach designed for subsequence matching, where the query is matched with a database subsequence without requiring the match length to be equal to the query length. SPRING has a lower computatio...

• Type: Conference
• Year: 2010
• Where: PETRA
• Authors: Jefrey Lijffijt, Panagiotis Papapetrou, Jaakko Hollmén, Vassilis Athitsos
• Keywords: Dynamic Time Warping, Emerging Technology, PETRA 2010, Subsequence Matching, Time Series