Add unittest for TriangularBands

test/src/unittest/spectral/test_triangularbands.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python
+
+# Copyright (C) 2006-2016  Music Technology Group - Universitat Pompeu Fabra
+#
+# This file is part of Essentia
+#
+# Essentia is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Affero General Public License as published by the Free
+# Software Foundation (FSF), either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the Affero GNU General Public License
+# version 3 along with this program. If not, see http://www.gnu.org/licenses/
+
+
+
+from essentia_test import *
+
+
+class TestTriangularBands(TestCase):
+    def testRegression(self):
+        # Simple regression test, comparing normal behaviour
+        audio = MonoLoader(filename = join(testdata.audio_dir, 'generated/synthesised/sin440_sweep_0db.wav'),
+                           sampleRate = 44100)()
+
+        fft = Spectrum()
+        window = Windowing(type = 'hamming')
+        fbands = TriangularBands(sampleRate = 44100)
+
+        for frame in FrameGenerator(audio, frameSize = 2048, hopSize = 512):
+            bands = fbands(fft(window(frame)))
+
+            self.assert_(not any(numpy.isnan(bands)))
+            self.assert_(not any(numpy.isinf(bands)))
+            self.assert_(all(bands >= 0.0))
+
+        # input is a flat spectrum:
+        input = [1]*1024
+        sr = 44100.
+        binfreq = 0.5*sr/(len(input)-1)
+        fbands = [8*x*binfreq for x in range(6)]
+        # expected energies in each band are 1 due to normalization
+        expected = [1, 1, 1, 1]
+        output = TriangularBands(frequencyBands=fbands, log=False)(input)
+        self.assertEqualVector(output, expected)
+
+    """
+    def testSingleBand(self):
+        # input is a flat spectrum
+        input = [1]*1024    
+        # frequency bin weights are normalized to sum up to 1 within each band 
+        # therefore, the expected value is 1
+        self.assertAlmostEqualVectorFixedPrecision(TriangularBands(frequencyBands=[0, 22050./2, 22050], log=False)(input), [1.], 6)
+
+    def testZero(self):
+        # Inputting zeros should return null band energies
+        nbands = [0, 100, 200, 300]
+        fband = TriangularBands(frequencyBands=nbands)
+
+        self.assertEqualVector(fband(zeros(1024)), zeros(len(nbands)-2))
+        self.assertEqualVector(fband(zeros(10)), zeros(len(nbands)-2))
+
+    def testInvalidParam(self):
+        # Test that we must give valid frequency ranges or order
+        self.assertConfigureFails(TriangularBands(), { 'frequencyBands':[] })
+        self.assertConfigureFails(TriangularBands(), { 'frequencyBands': [-1] })
+        self.assertConfigureFails(TriangularBands(), { 'frequencyBands': [100] })
+        self.assertConfigureFails(TriangularBands(), { 'frequencyBands': [100, 200] })
+        self.assertConfigureFails(TriangularBands(), { 'frequencyBands': [0, 200, 100] })
+        self.assertConfigureFails(TriangularBands(), { 'frequencyBands': [0, 200, 200, 400] })
+
+    def testEmpty(self):
+        # Test that FrequencyBands on an empty vector should return null band energies
+        self.assertComputeFails(TriangularBands(), [])
+    """
+
+suite = allTests(TestTriangularBands)
+
+if __name__ == '__main__':
+    TextTestRunner(verbosity=2).run(suite)