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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.
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https://hal.telecom-paris.fr/hal-02946907
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Submitted on : Wednesday, September 23, 2020 - 3:10:47 PM
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Slim Essid, Gael Richard, Bertrand David. MUSICAL INSTRUMENT RECOGNITION BASED ON CLASS PAIRWISE FEATURE SELECTION. International Conference on Music Information Retrieval (ISMIR), Oct 2004, Barcelona, Spain. ⟨hal-02946907⟩

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