void setup()
{
stream.setLabelsForAllDimensions({"x", "y", "z"});
useStream(stream);
useOutputStream(oStream);
calibrator.setCalibrateFunction(processAccelerometerData);
calibrator.addCalibrateProcess("Resting",
"Rest accelerometer on flat surface.", restingDataCollected);
useCalibrator(calibrator);
pipeline.addFeatureExtractionModule(FeatureApply(3, 1, dotProduct));
pipeline.addFeatureExtractionModule(TimeseriesBuffer(80, 1));
pipeline.addFeatureExtractionModule(FeatureApply(80, 1, stddev));
pipeline.addFeatureExtractionModule(FeatureApply(1, 1, threshold));
usePipeline(pipeline);
registerTuneable(t, 0, 1.0, "Walking Threshold",
"How much the accelerometer data needs to be "
"changing to be considered walking.");
}
void setup() {
stream.setLabelsForAllDimensions({"audio"});
pipeline.addFeatureExtractionModule(
FFT(kFftWindowSize, kFftHopSize,
DIM, FFT::HAMMING_WINDOW, true, false));
MFCC::Options options;
options.sample_rate = kSampleRate;
options.fft_size = kFftWindowSize / 2;
options.start_freq = 300;
options.end_freq = 3700;
options.num_tri_filter = 26;
options.num_cepstral_coeff = 12;
options.lifter_param = 22;
options.use_vad = true;
options.noise_level = noise_level;
pipeline.addFeatureExtractionModule(MFCC(options));
pipeline.setClassifier(SVM());
// GMM(16, true, false, 1, 100, 0.001));
// In post processing, we wait #n predicitons. If m out of n predictions are
// from the same class, we declare the class as the right one.
//
// n = (duration * sample_rate) / frame_size
// where duration = post_duration
// sample_rate = kSampleRate
// frame_size = kFftHopSize
// m = n * post_ratio
int num_predictions = post_duration / 1000 * kSampleRate / kFftHopSize;
pipeline.addPostProcessingModule(
ClassLabelFilter(num_predictions * post_ratio, num_predictions));
auto ratio_updater = [](double new_ratio) {
ClassLabelFilter* filter =
dynamic_cast<ClassLabelFilter*>(pipeline.getPostProcessingModule(0));
// Recalculate num_predictions as post_duration might have been changed
int num_predictions = post_duration / 1000 * kSampleRate / kFftHopSize;
filter->setMinimumCount(new_ratio * num_predictions);
};
auto duration_updater = [](int new_duration) {
ClassLabelFilter* filter =
dynamic_cast<ClassLabelFilter*>(pipeline.getPostProcessingModule(0));
// Recalculate num_predictions as post_duration might have been changed
int num_predictions = post_duration / 1000 * kSampleRate / kFftHopSize;
filter->setBufferSize(num_predictions);
};
auto noise_updater = [](int new_noise_level) {
MFCC *mfcc = dynamic_cast<MFCC*>(pipeline.getFeatureExtractionModule(1));
mfcc->setNoiseLevel(new_noise_level);
};
registerTuneable(noise_level, 0, 20,
"Noise Level",
"The threshold for the system to distinguish between "
"ambient noise and speech/sound",
noise_updater);
registerTuneable(post_duration, 0, 2000,
"Duration",
"Time (in ms) that is considered as a whole "
"for smoothing the prediction",
duration_updater);
registerTuneable(post_ratio, 0.0f, 1.0f,
"Ratio",
"The portion of time in duration that "
"should be from the same class",
ratio_updater);
useInputStream(stream);
useOutputStream(oStream);
usePipeline(pipeline);
useLeaveOneOutScoring(false);
setGUIBufferSize(kSampleRate);
}