کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382012 660722 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Methodology for automatic bioacoustic classification of anurans based on feature fusion
ترجمه فارسی عنوان
روش برای خودکار طبقه بندی آکوستیک زیستی anurans بر اساس همجوشی قابلیت
کلمات کلیدی
تجزیه و تحلیل صوتی بیولوژیکی؛ شناسایی طبقه بندی آکوستیک زیستی؛ ادغام داده های صوتی. SVM
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Data fusion of temporal and frequency domain acoustic information.
• Anurans identification approach based on the parameterization of its sound.
• Experimental methodology for anuran species recognition.
• To develop a tool for generating knowledge on Biodiversity Conservation.

The automatic recognition of anurans by their calls provides indicators of ecosystem health and habitat quality. This paper presents a new methodology for the acoustic classification of anurans using a fusion of frequency domain features, Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), with time domain features like entropy and syllable duration through intelligent systems. This methodology has been validated in three databases with a significant number of different species proving the strength of this approach. First, the audio recordings are automatically segmented into syllables which represent different anuran calls. For each syllable, both types of features are computed and evaluated separately as in previous works. In the experiments, a novel data fusion method has been used showing an increase of the classification accuracy which achieves an average of 98.80% ± 2.43 in 41 anuran species from AmphibiaWeb database, 96.90% ± 3.57 in 58 frogs from Cuba and 95.48% ± 4.97 in 100 anurans from southern Brazil and Uruguay; reaching a classification rate of 95.38% ± 5.05 for the aggregate dataset of 199 species.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 50, 15 May 2016, Pages 100–106
نویسندگان
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