Bird songs recognition using two-dimensional Mel-scale frequency cepstral coefficients

• Main Author: 林士棻
• Other Authors: 李建興
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/38302762655714685237

碩士 === 中華大學 === 資訊工程學系(所) === 94 === We propose a method to automatically identify birds from their sounds in this paper. First, each syllable corresponding to a piece of vocalization is segmented. The average LPCC (ALPCC), average MFCC (AMFCC), Static MFCC (SMFCC), Two-dimensional MFCC (TDMFCC), Dynamic two-dimensional MFCC (DTDMFCC) and TDMFCC+DTDMFCC over all frames in a syllable are calculated as the vocalization features. Linear discriminant analysis (LDA) is exploited to increase the classification accuracy at a lower dimensional feature vector space. A clustering algorithm, called progressive constructive clustering (PCC) algorithm, is used to divide the feature vectors which were computed from the same bird species into several subclasses. In our experiments, TDMFCC+DTDMFCC can achieve average classification accuracy 90% and 89% for 420 bird species and 561 bird species.