The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit(OMP),which aims to develop a robust Ecological Sounds Recognition(ESR)system.Firstly,the OMP is employed to spar...The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit(OMP),which aims to develop a robust Ecological Sounds Recognition(ESR)system.Firstly,the OMP is employed to sparsely decompose the original signal,thus the high correlation components are retained to reconstruct in the first stage.Then,according to the frequency distribution of both foreground sound and background noise,the signal can be compensated by the residual components in the second stage.Via the two-stage reconstruction,high non-stationary noises are effectively reduced,and the reconstruction precision of foreground sound is improved.At recognition stage,we employ deep belief networks to model the composite feature sets extracted from reconstructed signal.The experimental results show that the proposed approach achieved superior recognition performance on 60 classes of ecological sounds in different environments under different Signal-to-Noise Ratio(SNR),compared with the existing method.展开更多
针对生态环境中背景噪声对声音辨识产生干扰的问题,提出利用萤火虫算法优化匹配追踪的方法进行生态声音辨识。利用匹配追踪(MP)稀疏分解声音信号,在保留信号主体结构的前提下对其进行重构,减小噪声的影响。使用萤火虫(GSO)算法优化搜索...针对生态环境中背景噪声对声音辨识产生干扰的问题,提出利用萤火虫算法优化匹配追踪的方法进行生态声音辨识。利用匹配追踪(MP)稀疏分解声音信号,在保留信号主体结构的前提下对其进行重构,减小噪声的影响。使用萤火虫(GSO)算法优化搜索最佳匹配原子,实现MP快速分解。对重构信号提取Mel频率倒谱系数(MFCCs),MP时频特征及基音频率。结合支持向量机(SVM)对56种生态声音在不同环境和信噪比情况下进行分类识别。实验结果表明,与传统MFCC与SVM的方法相比,该方法对生态声音在不同信噪比下的识别性能得到不同程度的改善并且具有较好的抗噪性,尤其适合低信噪比(30 d B以下)噪声情境下使用。展开更多
介绍了一种基于随机森林算法和大规模声学特征的噪声环境下鸟声识别方法。实验基于由德国柏林自然科学博物馆提供的真实鸟声数据以及人工加入信噪比依次为-10 d B、-5 d B、0 d B、5 d B和10 d B的2种类型噪声(即真实环境的背景噪声和...介绍了一种基于随机森林算法和大规模声学特征的噪声环境下鸟声识别方法。实验基于由德国柏林自然科学博物馆提供的真实鸟声数据以及人工加入信噪比依次为-10 d B、-5 d B、0 d B、5 d B和10 d B的2种类型噪声(即真实环境的背景噪声和高斯白噪声),对60类亚种鸟声进行大规模声学特征提取并进行基于随机森林算法的机器学习。结果表明:该方法对2类噪声环境均具有良好的鲁棒性,并能在较低信噪比时仍具有较好的识别性能。展开更多
基金Supported by the National Natural Science Foundation of China(No.61075022)
文摘The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit(OMP),which aims to develop a robust Ecological Sounds Recognition(ESR)system.Firstly,the OMP is employed to sparsely decompose the original signal,thus the high correlation components are retained to reconstruct in the first stage.Then,according to the frequency distribution of both foreground sound and background noise,the signal can be compensated by the residual components in the second stage.Via the two-stage reconstruction,high non-stationary noises are effectively reduced,and the reconstruction precision of foreground sound is improved.At recognition stage,we employ deep belief networks to model the composite feature sets extracted from reconstructed signal.The experimental results show that the proposed approach achieved superior recognition performance on 60 classes of ecological sounds in different environments under different Signal-to-Noise Ratio(SNR),compared with the existing method.
文摘针对生态环境中背景噪声对声音辨识产生干扰的问题,提出利用萤火虫算法优化匹配追踪的方法进行生态声音辨识。利用匹配追踪(MP)稀疏分解声音信号,在保留信号主体结构的前提下对其进行重构,减小噪声的影响。使用萤火虫(GSO)算法优化搜索最佳匹配原子,实现MP快速分解。对重构信号提取Mel频率倒谱系数(MFCCs),MP时频特征及基音频率。结合支持向量机(SVM)对56种生态声音在不同环境和信噪比情况下进行分类识别。实验结果表明,与传统MFCC与SVM的方法相比,该方法对生态声音在不同信噪比下的识别性能得到不同程度的改善并且具有较好的抗噪性,尤其适合低信噪比(30 d B以下)噪声情境下使用。
文摘介绍了一种基于随机森林算法和大规模声学特征的噪声环境下鸟声识别方法。实验基于由德国柏林自然科学博物馆提供的真实鸟声数据以及人工加入信噪比依次为-10 d B、-5 d B、0 d B、5 d B和10 d B的2种类型噪声(即真实环境的背景噪声和高斯白噪声),对60类亚种鸟声进行大规模声学特征提取并进行基于随机森林算法的机器学习。结果表明:该方法对2类噪声环境均具有良好的鲁棒性,并能在较低信噪比时仍具有较好的识别性能。
