To catch a chorus: using chroma-based representations for audio thumbnailing
By Mark A. Bartsch, Gregory H. Wakefield. Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2001
323 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/15b4cb3d8ba287aceef249170af2d13ca6e5f47b
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
- D. Ellis
- Remco C. Veltkamp
- Malcolm Slaney
- Marc Leman
- Michael A. Casey
- Christophe Rhodes
- Graham E. Poliner
- Masataka Goto
In Signal Processing
- D. Ellis
- Malcolm Slaney
- Marc Leman
- Michael A. Casey
- Remco C. Veltkamp
- Christophe Rhodes
- Graham E. Poliner
- Masataka Goto
Citations in the corpus
- Content-Based Music Information Retrieval: Current Directions and Future Challenges (2008, methodology): “c) Pitch-Class Profile (Chromagram): Another common type of frequency folding is used to represent the energy due to each pitch class in twelfth-octave bands called a pitch-class profile (PCP) [20]–[ 22 ].”
- Identifying `Cover Songs' with Chroma Features and Dynamic Programming Beat Tracking (2007, methodology): “We choose to record a single feature vector per beat, and use twelve element ‘chroma’ features to capture both the dominant note (typically melody) as well as the broad harmonic accompaniment [7, 2].”