Comparison of the Efficiency of Time and Frequency Descriptors Based on Different Classification Conceptions

• Published in: Artificial Intelligence and Soft Computing pp. 491 - 502
• Main Authors: Tyburek, Krzysztof, Prokopowicz, Piotr, Kotlarz, Piotr, Michal, Repka
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• Subjects: Audio descriptors; Fuzzy classification; Fuzzy system; Linguistic modeling; MPEG 7; Neural networks
• ISBN: 9783319193236; 3319193236
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-19324-3_44

Extraction and detailed analysis of sound files using the MPEG 7 standard descriptors is extensively explored. However, an automatic description of the specific field of sounds of nature still needs an intensive research. This publication presents a comparison of effectiveness of time and frequency descriptors applied in recognition of species of birds by their voices. The results presented here are a continuation of the research/studies on this subject. Three different conceptions of classification - the WEKA system as classical tool, linguistically modelled fuzzy system and artificial neural network were used for testing the descriptors’ effectiveness. The analysed sounds of birds come from 10 different species of birds: Corn Crake, Hawk, Blackbird, Cuckoo, Lesser Whitethroat, Chiffchaff, Eurasian Pygmy Owl, Meadow Pipit, House Sparrow and Firecrest. For the analysis of the physical features of a song, MPEG 7 standard audio descriptors were used.