| Paper: | MLSP-P3.9 | ||
| Session: | Speech and Audio Processing | ||
| Time: | Wednesday, May 19, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Other Applications | ||
| Title: | CLASSIFICATION OF NON-SPEECH ACOUSTIC SIGNALS USING STRUCTURE MODELS | ||
| Authors: | Matthias Wolff; Dresden University of Technology | ||
| Constanze Tschöpe; Fraunhofer Institute for Nondestructive Testing | |||
| Dieter Hentschel; Fraunhofer Institute for Nondestructive Testing | |||
| Matthias Eichner; Dresden University of Technology | |||
| Rüdiger Hoffmann; Dresden University of Technology | |||
| Abstract: | Non-speech acoustic signals are widely used as input of systems for non-destructive testing. In this rapidly growing field, the signals have increasing complexity leading to the fact that powerful models are required. Methods like DTW and HMM, which are established in speech recognition, have been successfully used but are not sufficient in all cases. We propose the application of generalized structured Markov graphs (SMG). We describe a task independent structure learning technique which automatically adapts the models to the structure of the test signals. By two case studies using data from real applications, we demonstrate that our solution outperforms hand-tuned HMM structures in terms of class discrimination. | ||
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