Animal Sound Recognition Based on Double Feature of Spectrogram

LI Ying; HUANG Hongkeng; WU Zhibin

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Animal Sound Recognition Based on Double Feature of Spectrogram

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摘要

Due to existence of different environments and noises, the existing method is difficult to ensure the recognition accuracy of animal sound in low Signal-to-noise (SNR) conditions. To address these problems, we propose a double feature, which consists of projection feature and Local binary pattern variance (LBPV) feature, combined with Random forest (RF) for animal sound recognition. In feature extraction, an operation of projecting is made on spectrogram to generate the projection feature. Meanwhile, LBPV feature is generated by means of accumulating the corresponding variances of all pixels for every Uniform local binary pattern (ULBP) in the spectrogram. Short-time spectral estimation algorithm is used to enhance sound signals in severe mismatched noise conditions. In the experiments, we classify 40 kinds of common animal sounds under different SNRs with rain noise, traffic noise, and wind noise. As the experimental results show, the proposed framework consisting of short-time spectrum estimation, double feature, and RF, can recognize a wide range of animal sounds and still remains a recognition rate over 80％ even under 0dB SNR.

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《电子学报（英文版）》 |2019年第4期|667-673|共7页
作者
LI Ying; HUANG Hongkeng; WU Zhibin;
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作者单位

College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;

Key Laboratory of Information Security of Network Systems (Fuzhou University), Fuzhou 350116, China;

College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;

Key Laboratory of Information Security of Network Systems (Fuzhou University), Fuzhou 350116, China;

College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;

Key Laboratory of Information Security of Network Systems (Fuzhou University), Fuzhou 350116, China;

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Animal Sound Recognition Based on Double Feature of Spectrogram

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