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 shorttime 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.

In this paper,we consider the influence of complex background environments on the automatic recognition of animal sounds with low signal-to-noise ratios(SNRs).We propose a method for identifying low-SNR animal sounds in various background environments.In this method,the sound signal is decomposed by a Bark scale wavelet packet,and the decomposition coefficient is used to generate a spectrogram of the reconstructed signal,which is projected onto a spectrogram to generate a Bark spectral projection(BSP)feature.Random forests(RF)are then used to identify animal sounds with low SNRs.We classified 40 common animal sounds with different SNRs in noise environments such as flowing water,highway,wind,and loud speech.The experimental results show that by combining the proposed meth-ods of short-time spectrum estimation,BSP,and RF in various background environments with different SNRs,the mean identification rate for animal noises can reach 80.5％.In addition,a recognition rate above 60％can be maintained even at –10 dB.