梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)是一种符合人耳听觉特征,并与频率呈非线性对应关系的频谱特征,广泛应用在语音识别、音频特征分析等方面。对于目前广泛使用的通过单一特征进行音频分类的方法,存在分类准确...梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)是一种符合人耳听觉特征,并与频率呈非线性对应关系的频谱特征,广泛应用在语音识别、音频特征分析等方面。对于目前广泛使用的通过单一特征进行音频分类的方法,存在分类准确度低、处理速度慢等方面的不足,提出了基于梅尔频率倒谱的音频分类算法,该算法对音频设定采样率,获取音频的时间序列,并根据时间序列提取梅尔频率倒谱系数特征,将获取的二维特征值进行数据拟合、标准化处理。构建多层卷积神经网络模型,将标准化处理后的梅尔频率倒谱系数特征作为网络的输入,通过交叉熵验证的方法,对模型的输出进行分类。通过实验数据可知,梅尔频率倒谱系数特征通过多层卷积网络处理后,分类结果准确率达到92.8%,使用模型进行分类时,速度达到每个样本7 ms的耗时,模型能对音频进行准确快速的分类。展开更多
In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid...In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy.展开更多
文摘梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)是一种符合人耳听觉特征,并与频率呈非线性对应关系的频谱特征,广泛应用在语音识别、音频特征分析等方面。对于目前广泛使用的通过单一特征进行音频分类的方法,存在分类准确度低、处理速度慢等方面的不足,提出了基于梅尔频率倒谱的音频分类算法,该算法对音频设定采样率,获取音频的时间序列,并根据时间序列提取梅尔频率倒谱系数特征,将获取的二维特征值进行数据拟合、标准化处理。构建多层卷积神经网络模型,将标准化处理后的梅尔频率倒谱系数特征作为网络的输入,通过交叉熵验证的方法,对模型的输出进行分类。通过实验数据可知,梅尔频率倒谱系数特征通过多层卷积网络处理后,分类结果准确率达到92.8%,使用模型进行分类时,速度达到每个样本7 ms的耗时,模型能对音频进行准确快速的分类。
基金The National Natural Science Foundation of China(No.61603091)。
文摘In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy.
