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Conference papers
MUSICAL INSTRUMENT RECOGNITION BASED ON CLASS PAIRWISE FEATURE SELECTION
Abstract : In this work, musical instrument recognition is considered on solo music from real world performance. A large sound database is used that consists of musical phrases ex-cerpted from commercial recordings with different instrument instances, different players, and varying recording conditions. The proposed recognition scheme exploits class pairwise feature selection based on inertia ratio maximization. Moreover , new signal processing features based on octave band energy measures are introduced that prove to be useful. Classification is performed using Gaussian Mixture Models in a one vs one fashion in association with a data rescal-ing procedure as pre-processing. Experimental results show that substantial improvement in recognition success is thus achieved.
Cited literature [21 references]
https://hal.telecom-paris.fr/hal-02946907
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Submitted on : Wednesday, September 23, 2020 - 3:10:47 PM
Last modification on : Tuesday, October 19, 2021 - 11:16:11 AM
Long-term archiving on: : Thursday, December 3, 2020 - 4:08:54 PM
Contributor : Slim Essid Connect in order to contact the contributor
Submitted on : Wednesday, September 23, 2020 - 3:10:47 PM
Last modification on : Tuesday, October 19, 2021 - 11:16:11 AM
Long-term archiving on: : Thursday, December 3, 2020 - 4:08:54 PM
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