Audio sample path no valid

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Kamil Kacer
Kamil Kacer 2020-11-3
评论： Kamil Kacer 2020-11-5
function kNN_model_add_class(modelName, className, classPath, ...
listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% function kNN_model_add_class(modelName, className, classPath, ...
% listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% This function adds an audio class to the kNN classification model
%
% ARGUMENTS;
% - modelName: the filename of the model (mat file)
% - className: the name of the audio class to be added to the model
% - classPath: the path of the directory where the audio segments of the
% new class are stored
% - listOfStatistics: list of mid-term statistics (cell array)
% - stWin, stStep: short-term window size and step
% - mtWin, mtStep: mid-term window size and step
%
% Example:
% kNN_model_add_class('modelSpeech.mat', 'speech', './Music/', ...
% {'mean','std',}, 0.050, 0.025, 2.0, 1.0);
%
if ~exist(classPath,'dir')
error('Audio sample path is not valid!');
else
classPath = [classPath filesep];
end
% check if the model elaready exists:
fp = fopen(modelName, 'r');
if fp>0 % check if file already exists
load(modelName);
end
% Feature extraction:
D = dir([classPath '*.wav']);
F = [];
for (i=1:length(D)) % for each wav file in the given path:
curFileName = [classPath D(i).name];
FileNamesTemp{i} = curFileName;
% mid-term feature extraction for each wav file:
midFeatures = featureExtractionFile(curFileName, ...
stWin, stStep, mtWin, mtStep, listOfStatistics);
% long-term averaging:
longFeatures = mean(midFeatures,2);
F = [F longFeatures];
end
% save the model:
Statistics = listOfStatistics;
fp = fopen(modelName, 'r');
if fp<0 % model does not exist --> generate
ClassNames{1} = className;
Features{1} = F;
FileNames{1} = FileNamesTemp;
save(modelName, 'ClassNames', 'Features', ...
'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
else
load(modelName);
ClassNames{end+1} = className;
Features{end+1} = F;
FileNames{end+1} = FileNamesTemp;
save(modelName, 'ClassNames', 'Features', ...
'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
end
I have this function and when i try to call it with command
strDir = 'C:\Users\User\Desktop\3rd year\bachelor thesis\cats_dogs\train';
Statistics = {'mean', 'median', 'std', 'stdbymean', 'max', 'min'};
stWin = 0.040;
stStep = 0.040;
mtWin = 2;
mtStep = 1;
kNN_model_add_class('model8.mat', 'dog', [strDir '.dog/'], ...
Statistics, stWin, stStep, mtWin, mtStep);
It gives me an error... probably a path of srtDir is incorrectly written. Can you please help me.
Example of a code call would be great
  1 个评论
Walter Roberson
Walter Roberson 2020-11-3
does
C:\Users\User\Desktop\3rd year\bachelor thesis\cats_dogs\train.dog
exist as a directory?

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采纳的回答

Kamil Kacer
Kamil Kacer 2020-11-4
Now I get this error I am assuming i need to pass a struct to the function featureExtractionFile but I dont know how
so it can go throught every audio in directory.
Example of call would be appreciative
function [midFeatures, Centers, stFeaturesPerSegment] = ...
featureExtractionFile(signal, stWin, stStep, mtWin, mtStep, featureStatistics)
% function [midFeatures, Centers, stFeaturesPerSegment] = ...
% featureExtractionFile(fileName, stWin, stStep, mtWin, mtStep, ...
% featureStatistics)
%
% [mtFeatures, centers] = featureExtractionFile(fileName, ...
% 0.040, 0.040, 2.0, 1.0, {'mean','std'});
%
% This function reads a struct element and computes
% audio feature statitstics on a mid-term basis.
%
% ARGUMENTS:
% - signal: audio signal (struct)
% - stWin: short-term window size (in seconds)
% - stStep: short-term window step (in seconds)
% - mtWin: mid-term window size (in seconds)
% - mtStep: mid-term window step (in seconds)
% - featureStatistics: list of statistics to be computed (cell array)
%
% RETURNS
% - midFeatures [numOfFeatures x numOfMidTermWins] matrix
% (each collumn represents a mid-term feature vector)
% - Centers: representive centers for each
% mid-term window (in seconds)
% - stFeaturesPerSegment cell that contains short-term feature sequences
%
% (c) 2014 T. Giannakopoulos, A. Pikrakis
% convert mt win and step to ratio (compared to the short-term):
mtWinRatio = round(mtWin / stStep);
mtStepRatio = round(mtStep / stStep);
readBlockSize = 60; % one minute block size:
% get the length of the audio signal to be analyzed:
% ndret til struct brug!
a = signal.Filt_data;
fs = signal.SampleRate;
numOfSamples = length(a);
BLOCK_SIZE = round(readBlockSize * fs); % Antal samples per minut
curSample = 1;
count = 0;
midFeatures = [];
Centers = [];
stFeaturesPerSegment = {};
while (curSample <= numOfSamples) % while the end of file has not been reahed
% find limits of current block:
N1 = curSample;
N2 = curSample + BLOCK_SIZE - 1;
if (N2>numOfSamples)
N2 = numOfSamples;
end
tempX = signal.Filt_data(N1:N2,:); % ndret til struct brug!
% STEP 1: short-term feature extraction:
Features = stFeatureExtraction(tempX, fs, stWin, stStep);
% STEP 2: mid-term feature extraction:
[mtFeatures, st] = mtFeatureExtraction(...
Features, mtWinRatio, mtStepRatio, featureStatistics);
for (i=1:length(st))
stFeaturesPerSegment{end+1} = st{i};
end
Centers = [Centers readBlockSize * count + (0:mtStep:(N2-N1)/fs)];
midFeatures = [midFeatures mtFeatures];
% update counter:
curSample = curSample + BLOCK_SIZE;
count = count + 1;
end
if (length(Centers)==1)
Centers = (numOfSamples / fs) / 2;
else
C1 = Centers(1:end-1);
C2 = Centers(2:end);
Centers = (C1+C2) / 2;
end
if (size(midFeatures,2)>length(Centers))
midFeatures = midFeatures(:, 1:length(Centers));
end
if (size(midFeatures,2)<length(Centers))
Centers = Centers(:, 1:size(midFeatures,2));
end
  10 个评论
显示 8更早的评论
Walter Roberson
Walter Roberson 2020-11-5
function kNN_model_add_class(modelName, className, classPath, ...
listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% function kNN_model_add_class(modelName, className, classPath, ...
% listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% This function adds an audio class to the kNN classification model
%
% ARGUMENTS;
% - modelName: the filename of the model (mat file)
% - className: the name of the audio class to be added to the model
% - classPath: the path of the directory where the audio segments of the
% new class are stored
% - listOfStatistics: list of mid-term statistics (cell array)
% - stWin, stStep: short-term window size and step
% - mtWin, mtStep: mid-term window size and step
%
% Example:
% kNN_model_add_class('modelSpeech.mat', 'speech', './Music/', ...
% {'mean','std',}, 0.050, 0.025, 2.0, 1.0);
%
if ~exist(classPath,'dir')
error('Audio sample path is not valid!');
else
classPath = [classPath filesep];
end
% check if the model elaready exists:
fp = fopen(modelName, 'r');
if fp>0 % check if file already exists
load(modelName);
end
% Feature extraction:
D = dir([classPath '*.wav']);
F = [];
for (i=1:length(D)) % for each wav file in the given path:
curFileName = [classPath D(i).name];
FileNamesTemp{i} = curFileName;
[data, fs] = audioread(curFileName); %NEW
signal = struct('Filt_data', data, 'SampleRate', fs); %NEW
% mid-term feature extraction for each wav file:
midFeatures = featureExtractionFile(signal, ... %CHANGED
stWin, stStep, mtWin, mtStep, listOfStatistics);
% long-term averaging:
longFeatures = mean(midFeatures,2);
F = [F longFeatures];
end
% save the model:
Statistics = listOfStatistics;
fp = fopen(modelName, 'r');
if fp<0 % model does not exist --> generate
ClassNames{1} = className;
Features{1} = F;
FileNames{1} = FileNamesTemp;
save(modelName, 'ClassNames', 'Features', ...
'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
else
load(modelName);
ClassNames{end+1} = className;
Features{end+1} = F;
FileNames{end+1} = FileNamesTemp;
save(modelName, 'ClassNames', 'Features', ...
'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
end
Kamil Kacer
Kamil Kacer 2020-11-5
I just got it thank you so much

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更多回答（3 个）

Kamil Kacer
Kamil Kacer 2020-11-3
Yes this is the directory.
And also please how do i initialize or call featureExtractionFile function where i want to do audiowav by audioway as you see at the picture dog_barking0 dog_barking_1 etc
Walter Roberson
Walter Roberson 2020-11-3
replace
[strDir '.dog/']
with
fullfile(strDir, 'dog')
  1 个评论
Walter Roberson
Walter Roberson 2020-11-4
Your original code had
strDir = 'C:\Users\User\Desktop\3rd year\bachelor thesis\cats_dogs\train';
and I suggested you convert
kNN_model_add_class('model8.mat', 'dog', [strDir '.dog/'], ...
Statistics, stWin, stStep, mtWin, mtStep);
to
kNN_model_add_class('model8.mat', 'dog', fullfile(strDir, 'dog'), ...
Statistics, stWin, stStep, mtWin, mtStep);
You did convert the call, but you also changed to
strDir = 'C:\Users\User\Desktop\3rd year\bachelor thesis\cats_dogs\train\dogs';
You need to decide whether your strDir is the complete path to your training class (like the way you just changed it to) or if it is the path to the directory that contains your classes (like you had originally.)

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Kamil Kacer
Kamil Kacer 2020-11-3
I replaced it but it still gives me an error

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