Warping indexes with envelope transforms for query by humming

By Yunyue Zhu, Dennis E. Shasha. ACM SIGMOD Conference, 2003

294 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/03ce0503ad735d2aa455128247561a1beffc3963

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Tak-Chung Fu
2. Eamonn J. Keogh
3. Peter Scheuermann
4. Chotirat (Ann) Ratanamahatana
5. Raymond T. Ng
6. Lei Chen

In Signal Processing

1. Eamonn J. Keogh
2. Tak-Chung Fu
3. Rohit J. Kate
4. Chotirat (Ann) Ratanamahatana
5. Raymond T. Ng
6. Xiaoyue Wang
7. Goce Trajcevski
8. Peter Scheuermann

Citations in the corpus

• Dynamic Time Warping Algorithm Review (2008, unknown): “In this section we will focus on the “query-by-example” (QBE) [22] or “query-by-humming” [23] concept and specifically “time-boxes” [24] [25] and “sketching” [26] approaches which we are considering as the primary candidates for the future Hackystat module user-interaction model.”
• On The Marriage of Lp-norms and Edit Distance (2004, methodology): “In this section, we present experimental results based on the 24 benchmark data sets used in [31, 14], the stock data set used in [26], and the random walk data set tested in [16, 14, 31].”
• Three Myths about Dynamic Time Warping Data Mining (2005, methodology): “Some researchers believe that having wider warping window contributes to improvement in accuracy [22].”
• Tight Hardness Results for LCS and Other Sequence Similarity Measures (2015, background): “Because of its generality, DTWD has been successfully applied in a large variety of domains: automatic speech recognition [43], music information retrieval [39] , bioinformatics [1], medicine [16], identifying songs from humming [50], indexing of historical handwriting archives [45], databases [35],”
• A review on time series data mining (2011, methodology): “Different customized DTW techniques are applied to the field of music research for query by humming (Zhu and Shasha, 2003; Arentz et al., 2005 ).”
• Querying and mining of time series data: experimental comparison of representations and distance measures (2008, background): “For (at least) DFT, DWT and PAA, there are more than a dozen projects each that use the representations to index more than 100000 objects for query-by-humming [18, 45], Mo-Cap indexing [7] etc.”
• Making Time-Series Classification More Accurate Using Learned Constraints (2004, methodology): “And finally, in music, Zhu and Shasha (among many others [20]) have exploited DTW to query music databases with snippets of hummed phrases [46].”