基于MFCC的異常聲音識別技術研究
發(fā)布時間：2018-07-16 08:49
【摘要】：隨著社會的快速發(fā)展,一些潛在的矛盾沖突也伴隨出現,異常聲音逐漸引起人們的思索和重視。傳統(tǒng)安防迫切要求視頻監(jiān)控引入對異常聲音的判別,以提高智能性。長久以來,異常聲音的識別研究發(fā)展緩慢,遠遠落后于其它聲音的進展,主要是人們找不到刻畫異常聲音本質的特征參數。借鑒人耳聽覺特性在聽音辯物上的優(yōu)勢,越來越多模仿人耳聽覺感知的特征參數被提出,在信息科學領域已顯示出巨大發(fā)展?jié)摿�。本文針對采集的停車場幾種典型異常聲音,重點研究了一種基于Mel頻率倒譜系數(MFCC)及其改進的特征提取方法,并通過支持向量機完成各類異常聲音的識別分類。本文主要研究內容有:1、對采集到的異常聲音信號預處理,主要包括歸一化、預加重、分幀加窗。目的是消除采集過程中聲音樣本之間數值量級的差異,避免音量忽高忽低對整體聲音質量造成的影響,突出各個樣本的自身特征。由于異常聲音是非線性非平穩(wěn)的,要經過加窗分幀處理得到其短時信號。為了減少特征提取和識別階段中的運算量,進行端點檢測處理來確定異常聲音信號的起始點和結束點。2、針對預處理后的異常聲音信號,提取基于Mel頻率的倒譜特征參數。在特征提取過程中,利用人耳對聲音頻率感知的特性,構造一組類似耳蝸作用的三角濾波器,其作用是將每幀聲音信號頻譜能量由線性頻域映射到Mel頻域。然后對三角濾波器輸出的非線性頻譜進行對數變換,最后通過離散余弦變換映射在倒譜域上,完成MFCC特征參數的提取。3、在求取MFCC過程中,針對傅里葉變換有限的時頻分辨力以及計算過程中產生諧波干擾的缺陷,采用小波變換進行相應的改進,使得提取的特征參數更加符合人耳聽覺特性,改善對噪聲的魯棒性。同時,在特征提取過程中,引入經驗模態(tài)分解法,挖掘更多的動態(tài)特征,從而獲得復合改進的MFCC特征提取方法。4、完成異常聲音的分類識別。根據對異常聲音提取的特征,用支持向量機完成異常聲音模型的建立和測試。在訓練和測試階段,通過組合多個二分類SVM來實現多分類識別。根據參數對異常聲音模型泛化能力大小的影響,選擇合適的核函數類型,從而得到最佳的訓練模型,完成對測試樣本所屬類別的確定。
[Abstract]:With the rapid development of society, some potential conflicts also appear, abnormal sound gradually aroused people's thinking and attention. Traditional security requires video surveillance to introduce the discrimination of abnormal sound in order to improve intelligence. For a long time, the research on the recognition of abnormal sound has been slow and far behind the progress of other sounds, mainly because people can not find the characteristic parameters to describe the nature of abnormal sound. Drawing on the advantages of human auditory characteristics in audible speech, more and more characteristic parameters imitating human auditory perception have been proposed, which has shown great potential in the field of information science. In this paper, a new feature extraction method based on Mel frequency cepstrum coefficient (MFCC) and its improved feature extraction method is studied for several typical abnormal sounds collected from parking lot, and the recognition and classification of abnormal sounds are realized by support vector machine (SVM). The main contents of this paper are as follows: 1, preprocessing the collected abnormal sound signals, including normalization, preweighting, framing and windowing. The purpose of this paper is to eliminate the difference of numerical magnitude between sound samples in the process of acquisition, to avoid the influence of volume fluctuation and fluctuation on the overall sound quality, and to highlight the characteristics of each sample. Because the abnormal sound is nonlinear and non-stationary, the short-time signal is obtained by windowing. In order to reduce the computation in the stage of feature extraction and recognition, the endpoint detection is performed to determine the starting and ending point of abnormal sound signal, and the cepstrum feature parameters based on Mel frequency are extracted for the preprocessed abnormal sound signal. In the process of feature extraction, a group of triangular filters similar to cochlear interaction are constructed by using the human ear's perception of sound frequency. The function of the triangular filter is to map the spectral energy of each frame of sound signal from linear frequency domain to Mel frequency domain. Then logarithmic transformation of the nonlinear spectrum of the triangular filter output is carried out. Finally, the MFCC feature parameters are extracted by discrete cosine transform mapping in the cepstrum domain, and the MFCC feature parameters are extracted in the process of obtaining MFCC. In view of the finite time-frequency resolution of Fourier transform and the defects of harmonic interference in the calculation process, wavelet transform is adopted to make the extracted feature parameters more in line with the auditory characteristics of human ears and improve the robustness to noise. At the same time, in the process of feature extraction, the empirical mode decomposition method is introduced to mine more dynamic features, so as to obtain the compound improved MFCC feature extraction method .4, and complete the classification and recognition of abnormal sound. According to the feature of abnormal sound extraction, support vector machine (SVM) is used to build and test the abnormal sound model. In the stage of training and testing, multi-classification recognition is realized by combining multiple binary SVM. According to the influence of the parameters on the generalization ability of the abnormal sound model, the appropriate kernel function type is selected, and the best training model is obtained, and the classification of the test sample is determined.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN912.34
[Abstract]:With the rapid development of society, some potential conflicts also appear, abnormal sound gradually aroused people's thinking and attention. Traditional security requires video surveillance to introduce the discrimination of abnormal sound in order to improve intelligence. For a long time, the research on the recognition of abnormal sound has been slow and far behind the progress of other sounds, mainly because people can not find the characteristic parameters to describe the nature of abnormal sound. Drawing on the advantages of human auditory characteristics in audible speech, more and more characteristic parameters imitating human auditory perception have been proposed, which has shown great potential in the field of information science. In this paper, a new feature extraction method based on Mel frequency cepstrum coefficient (MFCC) and its improved feature extraction method is studied for several typical abnormal sounds collected from parking lot, and the recognition and classification of abnormal sounds are realized by support vector machine (SVM). The main contents of this paper are as follows: 1, preprocessing the collected abnormal sound signals, including normalization, preweighting, framing and windowing. The purpose of this paper is to eliminate the difference of numerical magnitude between sound samples in the process of acquisition, to avoid the influence of volume fluctuation and fluctuation on the overall sound quality, and to highlight the characteristics of each sample. Because the abnormal sound is nonlinear and non-stationary, the short-time signal is obtained by windowing. In order to reduce the computation in the stage of feature extraction and recognition, the endpoint detection is performed to determine the starting and ending point of abnormal sound signal, and the cepstrum feature parameters based on Mel frequency are extracted for the preprocessed abnormal sound signal. In the process of feature extraction, a group of triangular filters similar to cochlear interaction are constructed by using the human ear's perception of sound frequency. The function of the triangular filter is to map the spectral energy of each frame of sound signal from linear frequency domain to Mel frequency domain. Then logarithmic transformation of the nonlinear spectrum of the triangular filter output is carried out. Finally, the MFCC feature parameters are extracted by discrete cosine transform mapping in the cepstrum domain, and the MFCC feature parameters are extracted in the process of obtaining MFCC. In view of the finite time-frequency resolution of Fourier transform and the defects of harmonic interference in the calculation process, wavelet transform is adopted to make the extracted feature parameters more in line with the auditory characteristics of human ears and improve the robustness to noise. At the same time, in the process of feature extraction, the empirical mode decomposition method is introduced to mine more dynamic features, so as to obtain the compound improved MFCC feature extraction method .4, and complete the classification and recognition of abnormal sound. According to the feature of abnormal sound extraction, support vector machine (SVM) is used to build and test the abnormal sound model. In the stage of training and testing, multi-classification recognition is realized by combining multiple binary SVM. According to the influence of the parameters on the generalization ability of the abnormal sound model, the appropriate kernel function type is selected, and the best training model is obtained, and the classification of the test sample is determined.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN912.34
【參考文獻】
相關期刊論文 前10條
1 劉揚;張苗輝;鄭逢斌;;聽覺選擇性注意的認知神經機制與顯著性計算模型[J];計算機科學;2013年06期
2 胡峰松;曹孝玉;;基于Gammatone濾波器組的聽覺特征提取[J];計算機工程;2012年21期
3 王再歡;唐云建;韓鵬;;一種利用聲音識別的森林盜伐檢測方法[J];計算機工程與應用;2012年30期
4 李勇;李應;余清清;;新型MFCC和波動模型相結合的二層環(huán)境聲音識別[J];計算機工程與應用;2011年30期
5 張學鋒;王芳;夏萍;;融合LPC與MFCC的特征參數[J];計算機工程;2011年04期
6 欒少文;龔衛(wèi)國;;公共場所典型異常聲音的特征提取[J];計算機工程;2010年07期
7 王s，
本文編號：2125880
本文鏈接：http://sikaile.net/kejilunwen/wltx/2125880.html
教材專著
