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New Generation Computing
Erschienen in: New Generation Computing 4/2017

04.08.2017 | Special Feature

Automatic Classification of Impact Sounds with Rejection of Unknown Samples

verfasst von: Joaquim Ferreira da Silva, Sofia Cavaco, Gabriel Pereira Lopes

Erschienen in: New Generation Computing | Ausgabe 4/2017

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Abstract

The discrimination of very similar sounds is a hard task both for artificial systems and humans. For the former, the main problem lies on finding appropriate features to discriminate each class of sounds, which is an especially hard task when the sounds are very similar, such as impacts on rods of different metals. This paper presents a method to automatically select the features to be used in the classification. Given an initial large set of features, the method measures their discriminative power and builds a reduced set of new features which discriminates the sound classes very accurately. This feature selection method is part of the learning phase of a supervised classification approach also proposed here. In addition, this approach contains a module that rejects unknown sounds also very accurately. This is also an important innovation since most audio classifiers assume all test sounds belong to one of the known classes.
Vorheriger Artikel Tensor-Based Shot Boundary Detection in Video Streams
Nächster Artikel TexRep: A Text Mining Framework for Online Reputation Monitoring

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Fußnoten
1
Even though Fourier analysis gives information about both the magnitude and phase spectrum of the sound, here we consider only the magnitudes.
 
2
In the remaining text, given a set \(\mathscr {X}\), we define \(\Vert \mathscr {X}\Vert\) as its size.
 
3
In the remaining text (except for Sect. 5) every reasoning made for \(DP^{\prime }\) is valid for DP, unless otherwise specified or obviously not applicable.
 
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Metadaten
Titel
Automatic Classification of Impact Sounds with Rejection of Unknown Samples
verfasst von
Joaquim Ferreira da Silva
Sofia Cavaco
Gabriel Pereira Lopes
Publikationsdatum
04.08.2017
Verlag
Ohmsha
Erschienen in
New Generation Computing / Ausgabe 4/2017
Print ISSN: 0288-3635
Elektronische ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-017-0025-z

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