用多频带能量分布检测低信噪比声音事件

李应; 吴灵菲

doi:10.11999/JEIT180180

用多频带能量分布检测低信噪比声音事件

doi: 10.11999/JEIT180180

李应^,,
吴灵菲

1.
福州大学数学与计算机科学学院福州 350116
2.
网络系统信息安全福建省高校重点实验室福州 350116

基金项目: 国家自然科学基金(61075022)，福建省自然科学基金(2018J01793)

详细信息

作者简介:
李应：男，1964年生，教授，研究方向为信息安全、多媒体数据检索

吴灵菲：女，1994年生，硕士生，研究方向为信息安全、模式识别

通讯作者:
李应　 fj_liying@fzu.edu.cn

中图分类号: TP391.42
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- 被引次数: 0
出版历程
- 收稿日期: 2018-02-09
- 修回日期: 2018-07-09
- 网络出版日期: 2018-07-26
- 刊出日期: 2018-12-01

Detection of Sound Event under Low SNR Using Multi-band Power Distribution

Ying LI^,,
Lingfei WU

1.
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
2.
Fujian Province Key Laboratory of Information Security of Network Systems, Fuzhou University, Fuzhou 350116, China

Funds: The National Natural Science Foundation of China (61075022), The Natural Science Foundation of Fujian Province (2018J01793)

摘要

摘要: 该文针对低信噪比噪声环境下的声音事件检测问题，提出基于多频带能量分布图离散余弦变换的声音事件检测的方法。首先，将声音数据转化为gammatone频谱，并计算其多频带能量分布；接着，对多频带能量分布图进行8×8分块与离散余弦变换；然后，对8×8的离散余弦变换系数进行Zigzag扫描，抽取离散余弦变换系数的主要系数作为声音事件的特征；最后，利用随机森林分类器对特征建模与检测。实验结果表明，在低信噪比及各种噪声环境下，该文提出的方法具有良好的检测效果。
- 声音事件检测 /
- 多频带能量分布 /
- 随机森林 /
- 离散余弦变换
Abstract: As to the problem of sound event detection in low Signal-Noise-Ratio (SNR) noise environments, a method is proposed based on discrete cosine transform coefficients extracted from multi-band power distribution image. First, by using gammatone spectrogram analysis, sound signal is transformed into multi-band power distribution image. Next, 8×8 size blocking and discrete cosine transform are applied to analyze the multi-band power distribution image. Based on the main Zigzag coefficients which are scanned from the discrete cosine transform coefficients, features of sound event are constructed. Finally, features are modeled and detected through random forests classifier. The results show that the proposed method achieves a better detection performance in low SNR comparing to other methods.
- Sound event detection /
- Multi-band power distribution /
- Random forests /
- Discrete cosine transform

HTML全文

图 1 谱图特征用于非匹配条件的声音事件分类

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图 2 基于MBPD图的低信噪比声音事件检测

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图 3 茶隼叫声的gammatone频谱图及MBPD

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图 4 图像分块及DCT系数

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图 5 不同Z值的检测率

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图 6 MBPD-DCTZ特征在不同分类器下的检测率

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图 7 风声环境下–10 dB茶隼叫声、纯净茶隼叫声以及风声的波形图、gammatone频谱图和MBPD

下载: 全尺寸图片幻灯片

表 1 MBPD-DCTZ特征的交叉验证结果(%)

信噪比(dB)	噪声环境
信噪比(dB)	流水	粉噪声	风声	海浪	公路	雨声	平均
–10	40.0±0.7	65.7±5.1	32.5±3.8	44.7±0.9	52.6±3.8	36.5±3.2	45.3±11.1
–5	86.1±3.4	91.1±1.7	87.0±3.2	82.9±1.9	91.2±2.1	84.7±2.5	87.2±3.1
0	91.7±1.9	91.8±1.9	92.3±1.9	91.6±1.4	92.01±2.2	91.5±1.9	91.8±0.3
5	91.9±1.9	92.2±1.9	92.1±2.3	92.2±1.8	92.3±2.1	92.0±1.9	92.1±0.1

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表 3 不同特征对办公室声音事件的检测率(%)

特征	办公室声音事件	粉噪声信噪比(dB)
特征	办公室声音事件	5	0	–5
LBP	69.7±2.3	70.9±5.1	35.2±0.9	16.4±2.6
GLCM-SDH	47.3±5.4	44.2±7.5	45.5±5.4	38.8±4.8
HOG	70.3±5.2	40.6±4.8	33.9±3.1	32.1±2.3
MFCC	43.7±0.7	27.2±4.7	22.1±4.5	17.6±3.4
PNCC	47.2±1.9	34.3±2.0	28.1±2.3	22.1±1.8
MBPD-DCTZ	75.2±0.6	75.2±1.7	75.8±4.3	54.6±5.4

下载: 导出CSV

表 2 6种噪声环境下不同特征对动物声音事件的平均检测率(%)

特征	信噪比(dB)
特征	5	0	–5	–10
LBP	64.3±14.3	16.6±10.5	2.8±0.8	2.4±0.9
GLCM-SDH	41.4±3.5	36.0±4.3	14.6±9.5	4.2±1.7
HOG	68.9±5.4	28.8±10.5	7.4±5.2	4.1±1.8
MFCC	17.5±4.8	9.5±2.5	4.7±0.7	3.0±0.8
PNCC	28.0±0.9	20.0±0.9	9.1±2.0	2.5±0.8
MBPD-DCTZ	92.1±0.1	91.8±0.3	87.2±3.1	45.3±11.1

下载: 导出CSV

表 4 6种噪声环境下不同方法对动物声音事件的平均检测率(%)

方法	信噪比(dB)
方法	5	0	–5	–10
本文方法	92.1±0.1	91.8±0.3	87.2±3.1	45.3±11.1
MFCC-SVM^[22]	25.2±6.0	13.8±4.8	5.7±3.1	3.7±2.0
MP-SVM^[10]	30.0±2.5	16.4±4.0	8.2±2.4	4.6±0.9
SIF-SVM^[13]	61.4±8.5	40.3±12.1	18.9±13.4	9.7±7.7
SPD-KNN^[12]	87.9±1.8	82.7±3.9	45.4±22.1	9.9±8.8

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表 5 不同方法对办公室声音事件的检测率(%)

方法	办公室声音事件	粉噪声信噪比(dB)
方法	办公室声音事件	5	0	–5
本文方法	75.2±0.9	75.2±1.7	75.8±4.3	54.6±5.4
MFCC-SVM^[22]	16.4±1.8	15.8±1.7	17.6±0.9	16.4±3.0
MP-SVM^[10]	62.7±4.2	45.4±2.1	26.0±0.9	14.0±1.4
SIF-SVM^[13]	75.2±2.3	40.6±6.2	31.5±8.2	25.5±1.5
SPD-KNN^[12]	36.4±13.6	28.5±4.8	25.5±5.4	21.8±5.4

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