Musical instrument recognition using cepstral coefficients and temporal features
By Antti J. Eronen, Anssi Klapuri. 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000
308 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/c269fbb2b64034ffa77ca13757d3505b70fd23cf
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
- Xavier Serra
- Xavier Favory
- Frederic Font
- Jean-Julien Aucouturier
- François Pachet
- Eduardo Fonseca
- Eitan Wilf
- Karol J. Piczak
In Signal Processing
- Xavier Serra
- Xavier Favory
- Frederic Font
- Jean-Julien Aucouturier
- François Pachet
- Eduardo Fonseca
- Jordi Pons
- D. Ellis
Citations in the corpus
- Representing Musical Genre: A State of the Art (2003, background): “They are widely used as feature for speech recognition, and have also proved useful in musical instrument recognition (Eronen & Klapuri, 2000).”
- Improving Timbre Similarity : How high’s the sky ? (2004, background): “They are widely used as feature for speech recognition, and have also proved useful in musical instrument recognition ([10]).”
- Search Result Clustering in Collaborative Sound Collections (2020, methodology): “equencycontentofsoundsignals.Combiningthesedifferenttypes of features enables the representation of the timbre of musical instruments or more high-level characteristics such as music moods and genres [11, 18, 23, 31, 41]. Recently, techniques using Artificial Neural Networks have been able to provid”