Details

Autor(en) / Beteiligte

• Gemmeke, Jort F.
• Vuegen, Lode
• Karsmakers, Peter
• Vanrumste, Bart
• Van hamme, Hugo

Titel

An exemplar-based NMF approach to audio event detection

Ist Teil von

• 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2013, p.1-4

Ort / Verlag

IEEE

Erscheinungsjahr

2013

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• We present a novel, exemplar-based method for audio event detection based on non-negative matrix factorisation. Building on recent work in noise robust automatic speech recognition, we model events as a linear combination of dictionary atoms, and mixtures as a linear combination of overlapping events. The weights of activated atoms in an observation serve directly as evidence for the underlying event classes. The atoms in the dictionary span multiple frames and are created by extracting all possible fixed-length exemplars from the training data. To combat data scarcity of small training datasets, we propose to artificially augment the amount of training data by linear time warping in the feature domain at multiple rates. The method is evaluated on the Office Live and Office Synthetic datasets released by the AASP Challenge on Detection and Classification of Acoustic Scenes and Events.