Add peaking equalizer filter (#315)

Add peaking equalizer filter in functional.py and test it in test_functional_filter.py.

test/test_functional_filtering.py

@@ -136,6 +136,26 @@ class TestFunctionalFiltering(unittest.TestCase):
         # TBD - this fails at the 1e-4 level, debug why
         assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-3)
 
+    def test_equalizer(self):
+        """
+        Test biquad peaking equalizer filter, compare to SoX implementation
+        """
+
+        CENTER_FREQ = 300
+        Q = 0.707
+        GAIN = 1
+
+        noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.mp3")
+        E = torchaudio.sox_effects.SoxEffectsChain()
+        E.set_input_file(noise_filepath)
+        E.append_effect_to_chain("equalizer", [CENTER_FREQ, Q, GAIN])
+        sox_output_waveform, sr = E.sox_build_flow_effects()
+
+        waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True)
+        output_waveform = F.equalizer_biquad(waveform, sample_rate, CENTER_FREQ, GAIN, Q)
+
+        assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4)
+
     def test_perf_biquad_filtering(self):
 
         fn_sine = os.path.join(self.test_dirpath, "assets", "whitenoise.mp3")

torchaudio/functional.py

@@ -19,6 +19,7 @@ __all__ = [
     "lfilter",
     "lowpass_biquad",
     "highpass_biquad",
+    "equalizer_biquad",
     "biquad",
     'mask_along_axis',
     'mask_along_axis_iid'
@@ -685,6 +686,33 @@ def lowpass_biquad(waveform, sample_rate, cutoff_freq, Q=0.707):
     return biquad(waveform, b0, b1, b2, a0, a1, a2)
 
 
+def equalizer_biquad(waveform, sample_rate, center_freq, gain, Q=0.707):
+    # type: (Tensor, int, float, float, float) -> Tensor
+    r"""Designs biquad peaking equalizer filter and performs filtering.  Similar to SoX implementation.
+
+    Args:
+        waveform (torch.Tensor): audio waveform of dimension of `(channel, time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        center_freq (float): filter’s central frequency
+        gain (float): desired gain at the boost (or attenuation) in dB
+        q_factor (float): https://en.wikipedia.org/wiki/Q_factor
+
+    Returns:
+        output_waveform (torch.Tensor): Dimension of `(channel, time)`
+    """
+    w0 = 2 * math.pi * center_freq / sample_rate
+    A = math.exp(gain / 40.0 * math.log(10))
+    alpha = math.sin(w0) / 2 / Q
+
+    b0 = 1 + alpha * A
+    b1 = -2 * math.cos(w0)
+    b2 = 1 - alpha * A
+    a0 = 1 + alpha / A
+    a1 = -2 * math.cos(w0)
+    a2 = 1 - alpha / A
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
 @torch.jit.script
 def mask_along_axis_iid(specgrams, mask_param, mask_value, axis):
     # type: (Tensor, int, float, int) -> Tensor