[1]HUANG Hongkeng,LI Ying.Identifying low-SNR animal sounds based on Bark spectral projection[J].CAAI Transactions on Intelligent Systems,2018,13(4):610-618.[doi:10.11992/tis.201703008]
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Identifying low-SNR animal sounds based on Bark spectral projection
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 4
Page number:
610-618
Column:
学术论文—智能系统
Public date:
2018-07-05
- Title:
- Identifying low-SNR animal sounds based on Bark spectral projection
- Keywords:
- sound signal; automatic recognition; wavelet packet transform; random forests; environment sound
- CLC:
- TP391
- DOI:
- 10.11992/tis.201703008
- Abstract:
- 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 methods 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.
- References:
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Last Update:
2018-08-25