Identifying `Cover Songs' with Chroma Features and Dynamic Programming Beat Tracking
By D. Ellis, Graham E. Poliner. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2007
414 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/742c45248a6040b9c3906b780727efe132f540a4
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
- Remco C. Veltkamp
- Malcolm Slaney
- Marc Leman
- Michael A. Casey
- Christophe Rhodes
- Guojun Lu
- Dengsheng Zhang
- Zhouyu Fu
In Signal Processing
- Malcolm Slaney
- Marc Leman
- Michael A. Casey
- Remco C. Veltkamp
- Christophe Rhodes
- Guojun Lu
- Dengsheng Zhang
- Zhouyu Fu
Citations in the corpus
- Content-Based Music Information Retrieval: Current Directions and Future Challenges (2008, methodology): “Beat-based segmentation has been found to improve performance in cover song identification [ 28 ]”
- Semantic Annotation and Retrieval of Music and Sound Effects (2008, background): “(without derivatives), chroma features [30], and fluctuation pat-”
- Beat Tracking by Dynamic Programming (2007, methodology): “We have used this algorithm successfully as the basis of the beat-synchronous chroma features which underly our cover song detection system [Ellis and Poliner, 2007] which had the best performance by a wide margin in the MIREX-06 Cover Song Detection evaluation.”
- A Survey of Audio-Based Music Classification and Annotation (2011, methodology): “Pitch class features like pitch class profile (PCP) [97] and harmonic pitch class profile (HPCP) [98] have been developed and widely used in various tasks like melody analysis and transcription [93], [99], [100].”