鸟叫声识别matlab代码
时间: 2023-12-05 07:05:02 浏览: 90
由于鸟叫声识别是一个复杂的领域,需要大量的数据集和算法模型来实现。以下是一个简单的基于MFCC特征提取和KNN分类器的鸟叫声识别的MATLAB代码示例:
%% Load data
load('birdcalls.mat');
%% Feature extraction
fs = 44100; % Sampling rate
nfft = 1024; % FFT size
hopsize = 512; % Hop size
nmfcc = 13; % Number of MFCC coefficients
% Compute MFCC features for each sound
for i = 1:length(birdcalls)
[mfcc, ~] = melfcc(birdcalls{i}, fs, 'wintime', 0.025, 'hoptime', 0.010, 'numcep', nmfcc, 'maxfreq', fs/2, 'nfft', nfft);
features{i} = mfcc';
end
%% Classification
k = 5; % Number of neighbors for KNN classifier
test_idx = 10; % Index of test sound
% Compute distances from test sound to all training sounds
distances = zeros(length(birdcalls)-1, 1);
for i = 1:length(birdcalls)-1
if i < test_idx
features_train = features(1:i-1);
features_train = [features_train, features(i+1:end)];
else
features_train = features(1:test_idx-1);
features_train = [features_train, features(test_idx+1:i)];
end
features_train = cell2mat(features_train');
features_test = features{test_idx};
distances(i) = norm(features_train-features_test);
end
% Find k nearest neighbors
[~, idx] = sort(distances);
neighbors = idx(1:k);
% Compute majority vote
votes = zeros(length(birdcalls), 1);
for i = 1:k
if neighbors(i) < test_idx
votes(neighbors(i)) = votes(neighbors(i)) + 1;
else
votes(neighbors(i)+1) = votes(neighbors(i)+1) + 1;
end
end
% Predict class label
[~, class] = max(votes);
% Display result
disp(['Test sound is classified as: ' birdnames{class}]);