function [times, labels] = naive_threshold(x, fs, training)
% Deliberately weak floor: detect where the smoothed amplitude envelope
% peaks above a threshold, and classify every event by its duration alone
% (time the envelope stays above a fraction of the peak), nearest to the
% mean duration of each training type. Scores from this baseline are the
% floor real methods should clear.
nTypes = numel(training.snips);
L = size(training.snips{1}, 1);

w = round(0.005 * fs);
kern = ones(w, 1) / w;
env = conv(abs(x), kern, 'same');
n = numel(env);
thr = max(8 * median(env), 0.05 * max(env));
refr = round(L / 2);

% mean above-threshold duration per type, from the training snippets
durs = zeros(nTypes, 1);
for k = 1:nTypes
  S = training.snips{k};
  dk = 0;
  for j = 1:size(S, 2)
    ev = conv(abs(S(:, j)), kern, 'same');
    dk = dk + sum(ev > 0.2 * max(ev));
  end
  durs(k) = dk / max(size(S, 2), 1);
end

times = zeros(0, 1);
labels = zeros(0, 1);
i = 2;
while i < n
  if env(i) > thr && env(i-1) <= thr
    j2 = min(n, i + refr - 1);
    [pk, rel] = max(env(i:j2));
    p = i + rel - 1;
    j3 = min(n, i + L - 1);
    dur = sum(env(i:j3) > 0.2 * pk);
    [~, lab] = min(abs(durs - dur));
    times(end+1, 1) = (i - 1) / fs;
    labels(end+1, 1) = lab;
    i = p + refr;
  else
    i = i + 1;
  end
end
end