AES E-Library

AES E-Library

Feature Learning for Classifying Drum Components from Nonnegative Matrix Factorization

This paper explores automatic feature learning methods to classify percussive components in nonnegative matrix factorization (NMF). To circumvent the necessity of designing appropriate spectral and temporal features for component clustering, as usually used in NMF-based transcription systems, multilayer perceptrons and deep belief networks are trained directly on the factorization of a large number of isolated samples of kick and snare drums. The learned features are then used to assign components resulting from the analysis of polyphonic music to the different drum classes and retrieve the temporal activation curves. The evaluation on a set of 145 excerpts of polyphonic music shows that the algorithms can efficiently classify drum components and compare favorably to a classic “bag-of-features” approach using support vector machines and spectral mid-level features.

AES Convention: Paper Number:
Publication Date:

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location:

Start a discussion about this paper!

AES - Audio Engineering Society