Chris@126: 
Chris@126: #include "FeatureExtractor.h"
Chris@126: 
Chris@126: #include <vector>
Chris@126: #include <iostream>
Chris@126: #include <cmath>
Chris@126: 
Chris@126: using namespace std;
Chris@126: 
Chris@126: #define BOOST_TEST_DYN_LINK
Chris@126: #define BOOST_TEST_MAIN
Chris@126: 
Chris@126: #include <boost/test/unit_test.hpp>
Chris@126: 
Chris@126: static int freq2mid(double freq)
Chris@126: {
Chris@165:     return int(round(57.0 + 12.0 * log(freq / 220.) / log(2.)));
Chris@126: }
Chris@126: 
Chris@126: static int freq2chroma(double freq)
Chris@126: {
Chris@126:     return freq2mid(freq) % 12;
Chris@126: }
Chris@126: 
Chris@127: static int bin2warped(int bin, int rate, int sz)
Chris@127: {
Chris@127:     // see comments in nonchroma below
Chris@127:     if (bin <= 33) return bin;
Chris@127:     double freq = (double(bin) * rate) / sz;
Chris@127:     int mid = freq2mid(freq);
Chris@127:     if (mid > 127) mid = 127;
Chris@127:     int outbin = mid - 77 + 33;
Chris@127:     return outbin;
Chris@127: }
Chris@127: 
Chris@126: BOOST_AUTO_TEST_SUITE(TestFeatureExtractor)
Chris@126: 
Chris@185: void checkAlternateProcessType(FeatureExtractor &fe, feature_t expected,
Chris@130: 			       vector<double> real, vector<double> imag)
Chris@130: {
Chris@130:     vector<float> in;
Chris@188:     for (size_t i = 0; i < real.size(); ++i) {
Chris@130: 	in.push_back(float(real[i]));
Chris@130: 	in.push_back(float(imag[i]));
Chris@130:     }
Chris@185:     feature_t alt = fe.process(&in[0]);
Chris@130:     BOOST_CHECK_EQUAL_COLLECTIONS(alt.begin(), alt.end(),
Chris@130: 				  expected.begin(), expected.end());
Chris@130: }
Chris@130: 
Chris@126: BOOST_AUTO_TEST_CASE(chroma)
Chris@126: {
Chris@129:     int szs[] = { 1024, 2048, 4000 };
Chris@129:     int rates[] = { 44100, 48000 };
Chris@126:     
Chris@129:     for (int irate = 0; irate < int(sizeof(rates)/sizeof(rates[0])); ++irate) {
Chris@126: 
Chris@129: 	int rate = rates[irate];
Chris@126: 
Chris@129: 	for (int isz = 0; isz < int(sizeof(szs)/sizeof(szs[0])); ++isz) {
Chris@129: 	    
Chris@129: 	    int sz = szs[isz];
Chris@129: 
Chris@129: 	    int hs = sz / 2 + 1;
Chris@129: 	    int fsz = 13;
Chris@129:     
Chris@216: 	    FeatureExtractor::Parameters params { float(rate) };
Chris@216: 	    params.fftSize = sz;
Chris@129: 	    params.useChromaFrequencyMap = true;
Chris@196: 	    params.minFrequency = 0;
Chris@129: 	    FeatureExtractor fe(params);
Chris@129: 	    BOOST_CHECK_EQUAL(fe.getFeatureSize(), fsz);
Chris@129: 
Chris@129: 	    for (int bin = 0; bin < hs; ++bin) {
Chris@129: 
Chris@129: 		vector<double> real, imag;
Chris@129: 		real.resize(hs, 0.0);
Chris@129: 		imag.resize(hs, 0.0);
Chris@126: 	
Chris@129: 		real[bin] += 10.0;
Chris@129: 		imag[bin] += 10.0;
Chris@126: 
Chris@129: 		// use two input sweeps, so we can test that they are
Chris@129: 		// properly summed into the output bin
Chris@129: 		real[hs-bin-1] += 5.0;
Chris@129: 		imag[hs-bin-1] += 5.0;
Chris@129: 	
Chris@185: 		feature_t out = fe.process(real, imag);
Chris@126: 
Chris@130: 		checkAlternateProcessType(fe, out, real, imag);
Chris@130: 
Chris@129: 		// We expect to find all bins are 0 except for:
Chris@129: 		// 
Chris@129: 		// * two bins of 200 and 50 respectively, if the two input
Chris@129: 		// bins are distinct and their output chroma are also distinct
Chris@129: 		//
Chris@129: 		// * one bin of value 250 (= 10^2 + 5^2), if the two input
Chris@129: 		// bins are distinct but their output chroma are not
Chris@129: 		//
Chris@129: 		// * one bin of value 450 (= 15^2 + 15^2), if the input bins
Chris@129: 		// are not distinct (the feature extractor sums energies
Chris@129: 		// rather than magnitudes so as to integrate energy for a
Chris@129: 		// partial in the face of spectral leakage)
Chris@129: 		// 
Chris@129: 		// The bin corresponding to each input frequency is
Chris@129: 		// that of its semitone value (with C=0), except that
Chris@129: 		// input bins less than the 17th are shepherded into
Chris@129: 		// the separate bin 0 (see docs in FeatureExtractor.h)
Chris@126: 
Chris@129: 		double cutoff = (17.0 * rate) / sz;
Chris@129: 		
Chris@185: 		feature_t expected(fsz);
Chris@126: 
Chris@129: 		double infreq1 = (double(bin) * rate) / sz;
Chris@126: 
Chris@129: 		if (bin == hs-bin-1) {
Chris@129: 		    expected[freq2chroma(infreq1) + 1] += 450;
Chris@129: 		} else {
Chris@129: 		    if (infreq1 < cutoff) {
Chris@129: 			expected[0] += 200;
Chris@129: 		    } else {
Chris@129: 			expected[freq2chroma(infreq1) + 1] += 200;
Chris@129: 		    }
Chris@129: 		    double infreq2 = (double(hs-bin-1) * rate) / sz;
Chris@129: 		    if (infreq2 < cutoff) {
Chris@129: 			expected[0] += 50;
Chris@129: 		    } else {
Chris@129: 			expected[freq2chroma(infreq2) + 1] += 50;
Chris@129: 		    }
Chris@129: 		}
Chris@126: 
Chris@129: 		BOOST_CHECK_EQUAL_COLLECTIONS(out.begin(), out.end(),
Chris@129: 					      expected.begin(), expected.end());
Chris@126: 	    }
Chris@126: 	}
Chris@126:     }
Chris@126: }
Chris@126: 
Chris@127: BOOST_AUTO_TEST_CASE(nonchroma)
Chris@127: {
Chris@127:     int rate = 44100;
Chris@127:     int sz = 2048;
Chris@127:     int hs = sz / 2 + 1;
Chris@127:     int fsz = 84;
Chris@127:     
Chris@216:     FeatureExtractor::Parameters params { float(rate) };
Chris@216:     params.fftSize = sz;
Chris@196:     params.minFrequency = 0;
Chris@127:     FeatureExtractor fe(params);
Chris@127:     BOOST_CHECK_EQUAL(fe.getFeatureSize(), fsz);
Chris@127: 
Chris@127:     for (int bin = 0; bin < hs; ++bin) {
Chris@127: 
Chris@127: 	vector<double> real, imag;
Chris@127: 	real.resize(hs, 0.0);
Chris@127: 	imag.resize(hs, 0.0);
Chris@127: 	
Chris@127: 	real[bin] += 10.0;
Chris@127: 	imag[bin] += 10.0;
Chris@127: 
Chris@127: 	// use two input sweeps, so we can test that they are properly
Chris@127: 	// summed into the output bin
Chris@127: 	real[hs-bin-1] += 5.0;
Chris@127: 	imag[hs-bin-1] += 5.0;
Chris@127: 
Chris@185: 	feature_t out = fe.process(real, imag);
Chris@127: 
Chris@130: 	checkAlternateProcessType(fe, out, real, imag);
Chris@130: 
Chris@127: 	// We expect to find all bins are 0 except for:
Chris@127: 	// 
Chris@127: 	// * two bins of 200 and 50 respectively, if the two input
Chris@127: 	// bins are distinct and their output bins are also distinct
Chris@127: 	//
Chris@127: 	// * one bin of value 250 (= 10^2 + 5^2), if the two input
Chris@127: 	// bins are distinct but their output bins are not
Chris@127: 	//
Chris@127: 	// * one bin of value 450 (= 15^2 + 15^2), if the input bins
Chris@128: 	// are not distinct (the feature extractor sums energies
Chris@128: 	// rather than magnitudes so as to integrate energy for a
Chris@128: 	// partial in the face of spectral leakage)
Chris@127: 	//
Chris@127: 	// The first 34 input bins (i.e. up to and including bin 33,
Chris@127: 	// 733Hz, MIDI pitch 77.something) are mapped linearly, those
Chris@127: 	// above that and up to MIDI pitch 127 (12544Hz) are mapped
Chris@127: 	// logarithmically, remaining bins are all mapped into the
Chris@127: 	// final output bin.
Chris@127: 	//
Chris@127: 	// So MIDI pitches up to and including 77 are mapped linearly
Chris@127: 	// by frequency into 34 bins; those from 78-126 inclusive are
Chris@127: 	// mapped linearly by MIDI pitch into the next 49 bins;
Chris@127: 	// everything above goes into the last bin, for 84 bins total.
Chris@127: 
Chris@185: 	feature_t expected(fsz);
Chris@127: 
Chris@127: 	if (bin == hs-bin-1) {
Chris@127: 	    expected[bin2warped(bin, rate, sz)] += 450;
Chris@127: 	} else {
Chris@127: 	    expected[bin2warped(bin, rate, sz)] += 200;
Chris@127: 	    expected[bin2warped(hs-bin-1, rate, sz)] += 50;
Chris@127: 	}
Chris@127: 
Chris@127: 	BOOST_CHECK_EQUAL_COLLECTIONS(out.begin(), out.end(),
Chris@127: 				      expected.begin(), expected.end());
Chris@127:     }
Chris@127: }
Chris@127: 
Chris@126: BOOST_AUTO_TEST_SUITE_END()