【MWSG】滤波器增强频谱图，实现强大的鸟声检测（Matlab代码实现）

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

🎉3 参考文献

🌈4 Matlab代码实现

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💥1 概述

在人类的感知系统中，视觉感知和听觉感知是两种主要感知手段，其中，声音是传递信息的重要媒介，也是听觉感知系统的基本要素之一。当前，随着人工智能技术的迅速发展，机器视觉、计算机视觉等方面的技术虽趋于完善，但机器听觉的主要研究对象是语音和声纹，其对复杂声音事件分类和检测等领域的研究仍相对匮乏。目前声音事件分类主要应用于公共安全智能化监控、异常声音检测、城市噪音检测等领域。但当前已有的声音事件分类和定位检测模型仍存在分类准确率不够高、定位精度较低以及检测错误率较高等问题，本文提出一种有效的先验信噪比平滑方法。该方法从先验信噪比的定义出发，使用小波阈值多窗口功率谱估计方法减小语音功率谱和噪声谱的方差，从而实现先验信噪比的平滑。并运用一种无监督系统，使用增强型多窗口萨维茨基-戈雷 （MWSG） 频谱图进行稳健的鸟类声音检测。

📚2 运行结果

部分代码：

%%
% This file is included to demonstrate the steps of algorithm we used to
% detect Bird sounds using Multiple Window Savitzky-Golay(MWSG) Filter
close all;
clear all;
clc;
%% Step 1 MWSG Filter
% Compute the MWSG spectrogram for the given audio signal
disp('Reading wav File');
[signal,fs]=audioread('./PC5_20090606_050000_0010.wav'); %% MLSP audio file
% Parameters
M=21; %Matrix length required to calculate SG coefficents
P=3; %Order required to calculate SG coefficents
nfft=512; %FFT Order
shift=256; % Shift
winlength=512;% Window Length
disp('Computing MWSG Spectrogram');
MWSG=compute_MWSG_Spec(signal,fs,M,P);
%% Step 2 Directionality
% Calculate the directional spectrograms based on MWSG Spectrogram
% Parameters
len=11; % No of array values to be summed up in the required direction
disp('Computing directional Spectrograms on MWSG Spectrogram');
[x_D1,x_D2,x_D3,x_D4,DAll]=compute_Dir_Spec_From_MWSG(MWSG,len);
%% Step 3 Segmentation
% Calculating Predicted frames for each directed spectrogam
disp('Computing Predicted Frames');
d1=segment(x_D1); % Predicted frames at 0 degrees directed spectrogram
d2=segment(x_D2); % Predicted frames at 0 degrees directed spectrogram
d3=segment(x_D3); % Predicted frames at 0 degrees directed spectrogram
d4=segment(x_D4); % Predicted frames at 0 degrees directed spectrogram
% Final Predicted frames(d) = max(each directional predicted frame)
d= (d1+d3+d2+d4);
d(d>0)=1;

%% Figures
% Just to get Frequency and Time Points
[~,F,T,~]=spectrogram(signal,winlength,shift,nfft,fs);
% Loading ground truth frames
disp('Reading GroundTruth');
load('./GroundTruth.txt');
disp('Saving Figures');
h = figure;
set(h, 'Visible', 'off');
subplot(3,1,1);
time=(1:length(signal))/fs;
plot(time,signal); % Signal
title('Signal');
xlabel('Time in sec');
ylabel('Amplitute');
subplot(3,1,2);
surf(T,F,10*log10(MWSG),'EdgeColor','none'); %MWSG Spectrogram
view(0,90);
axis tight;
title('MWSG Spectrogram');
xlabel('Time in sec');
ylabel('Frequency in Hz');
subplot(3,1,3);
plot(T,GroundTruth,'r'); % GroundTruth Frames
hold on;
plot(T,d,'b');   % Predicted Frames
hold off;
ylim([0 2]);
xlabel('Time in sec');
legend('GroundTruth','Predicted Frames');
saveas(h,'./MWSG_Spectrogram.png');

🎉3 参考文献

部分理论来源于网络，如有侵权请联系删除。

[1]Nithin Rao, Nisha G Meenakshi, Prasanta Kumar Ghosh (2017) Spectrogram enhancement using multiple window Savitzky Golay (MWSG) filter for robust bird sound detection [Source Code]. 

[2]焦人杰,侯丽敏.基于小波阈值多窗口功率谱估计的语音增强[J].上海大学学报(自然科学版),2008(03):230-235.

🌈4 Matlab代码实现