基于HFCC的鸟类物种识别

Robert Wielgat, T. Zieliński, T. Potempa, A. Lisowska-Lis, D. Król
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引用次数: 16

摘要

本文介绍了波兰鸟类种类识别的初步研究结果。鸟类的声音是在一个高度嘈杂的城市环境中录制的。为了记录鸟类的声音,使用了96千赫的高采样频率。作为特征集,选取了标准mel-frequency倒谱系数(MFCC)和最近提出的human-factor倒谱系数(HFCC)参数。HFCC特性优于MFCC特性。在HFCC特征提取过程中,适当限制最大频率可以提高鸟类物种识别的准确性。初步结果良好,为本文方法在鸟类保护区监测中的实际应用提供了良好的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HFCC based recognition of bird species
Results from preliminary research on recognition of Polish birds' species are presented in the paper. Bird voices were recorded in a highly noised municipal environment. High 96 kHz sampling frequency has been used in order to record birds' voices. As a feature set standard mel-frequency cepstral coefficients (MFCC) and recently proposed human-factor cepstral coefficients (HFCC) parameters were selected. Superior performance of the HFCC features over MFCC ones has been observed. Proper limiting of the maximal frequency during HFCC feature extraction results in increasing accuracy of birds' species recognition. Good initial results are very promising for practical application of the methods described in the paper in monitoring of protected birds' area.
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