Automatic Classification of Impact Sounds with Rejection of Unknown Samples

Joaquim Ferreira da Silva, Sofia Cavaco, Gabriel Pereira Lopes

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)341-363
Number of pages23
JournalNew Generation Computing
Issue number4
Publication statusPublished - 1 Oct 2017


  • Feature selection
  • Impact sounds classification
  • Machine learning


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