Model selection criterion is an unsupervised technique and can be used 
to compare statistical distribution of the data. In this paper the experiments 
of using model selection criterion for audio analysis tasks are presented.  
This technique is applied for direct audio search in German broadcasts news 
with the high variability in duration and loudness of the search patterns. 
Using model selection criterion as a distance metric the experiments 
for identification of 14 environmental sounds are carried out. 
For environment sounds detection the decision is based on mutual similarity 
of compared events to the set of reference events. For audio events recognition 
Latent Semantic Indexing (LSI) is also tested. Approximately 500 audio 
segments from 14 sound types are used in the recognition test. 
The experiments show that the applications of model selection criterion 
for direct audio search, unsupervised environmental sounds analysis and 
sounds recognition using LSI are effective and accurate.