<p>The tasks : discriminating music, speech and other sounds and language identification have a broad range of applications in todays multilingual multimedia community. Both tasks gave a lot of possibilities regarding methods and development tools which also brings some risk. The Language Identification(LID) problem ended up with two different approaches. One approach was discarded due to poor results in the pre-study while the other approach had some promising potential but did not deliver as hoped in the first place. On the other hand, the music, speech discrimination was solved with great accuracy using 3 simple time domain features and Support Vector Machines(SVM). Adding 'other sounds' to this discrimination problem did complicate the problem but the final solution delivered great results using the enormous BBC Sound Effects library as examples of non speech and music. Both tasks were tried being solved using Gaussian Mixture Models(GMM) because of it's known great ability to model arbitrary feature space segmentations. The tools used were Matlab together with a number of different toolboxes explained further in the text.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:ntnu-8953 |
| Date | January 2008 |
| Creators | Strømhaug, Tommy |
| Publisher | Norwegian University of Science and Technology, Department of Computer and Information Science, Institutt for datateknikk og informasjonsvitenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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