To access the full text documents, please follow this link:

A fast one-pass-training feature selection technique for GMM-based acoustic event detection with audio-visual data
Butko, Taras; Nadeu Camprubí, Climent
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
Acoustic event detection becomes a difficult task, even for a small number of events, in scenarios where events are produced rather spontaneously and often overlap in time. In this work, we aim to improve the detection rate by means of feature selection. Using a one-against-all detection approach, a new fast one-pass-training algorithm, and an associated highly-precise metric are developed. Choosing a different subset of multimodal features for each acoustic event class, the results obtained from audiovisual data collected in the UPC multimodal room show an improvement in average detection rate with respect to using the whole set of features.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
Acoustic emission -- Mathematical models
Esdeveniments sonors -- Models matemàtics
Article - Draft

Show full item record

Related documents

Other documents of the same author

Butko, Taras; Canton Ferrer, Cristian; Segura Perales, Carlos; Giró Nieto, Xavier; Nadeu Camprubí, Climent; Hernando Pericás, Francisco Javier; Casas Pla, Josep Ramon
Butko, Taras; Nadeu Camprubí, Climent; Schulz, Henrik