Add frequency_impulse_response

docs/source/prototype.functional.rst

@@ -50,3 +50,4 @@ DSP
    extend_pitch
    oscillator_bank
    sinc_impulse_response
+   frequency_impulse_response

test/torchaudio_unittest/prototype/functional/autograd_test_impl.py

@@ -87,3 +87,7 @@ def test_deemphasis(self):
         coeff = 0.9
         self.assertTrue(gradcheck(F.deemphasis, (waveform, coeff)))
         self.assertTrue(gradgradcheck(F.deemphasis, (waveform, coeff)))
+
+    def test_freq_ir(self):
+        mags = torch.tensor([0, 0.5, 1.0], device=self.device, dtype=self.dtype, requires_grad=True)
+        assert gradcheck(F.frequency_impulse_response, (mags,))

test/torchaudio_unittest/prototype/functional/dsp_utils.py

@@ -35,3 +35,9 @@ def sinc_ir(cutoff: ArrayLike, window_size: int = 513, high_pass: bool = False):
         filt *= -1
         filt[..., half] = 1.0 + filt[..., half]
     return filt
+
+
+def freq_ir(magnitudes):
+    ir = np.fft.fftshift(np.fft.irfft(magnitudes), axes=-1)
+    window = np.hanning(ir.shape[-1])
+    return (ir * window).astype(magnitudes.dtype)

test/torchaudio_unittest/prototype/functional/functional_test_impl.py

@@ -8,7 +8,7 @@
 from torchaudio.functional import lfilter
 from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
 
-from .dsp_utils import oscillator_bank as oscillator_bank_np, sinc_ir as sinc_ir_np
+from .dsp_utils import freq_ir as freq_ir_np, oscillator_bank as oscillator_bank_np, sinc_ir as sinc_ir_np
 
 
 def _prod(l):
@@ -470,6 +470,22 @@ def test_preemphasis_deemphasis_roundtrip(self, input_shape, coeff):
         deemphasized = F.deemphasis(preemphasized, coeff=coeff)
         self.assertEqual(deemphasized, waveform)
 
+    def test_freq_ir_warns_negative_values(self):
+        """frequency_impulse_response warns negative input value"""
+        magnitudes = -torch.ones((1, 30), device=self.device, dtype=self.dtype)
+        with self.assertWarnsRegex(UserWarning, "^.+should not contain negative values.$"):
+            F.frequency_impulse_response(magnitudes)
+
+    @parameterized.expand([((2, 3, 4),), ((1000,),)])
+    def test_freq_ir_reference(self, shape):
+        """frequency_impulse_response produces the same result as reference implementation"""
+        magnitudes = torch.rand(shape, device=self.device, dtype=self.dtype)
+
+        hyp = F.frequency_impulse_response(magnitudes)
+        ref = freq_ir_np(magnitudes.cpu().numpy())
+
+        self.assertEqual(hyp, ref)
+
 
 class Functional64OnlyTestImpl(TestBaseMixin):
     @nested_params(

test/torchaudio_unittest/prototype/functional/torchscript_consistency_test_impl.py

@@ -98,3 +98,7 @@ def test_deemphasis(self):
         waveform = torch.rand(3, 2, 100, device=self.device, dtype=self.dtype)
         coeff = 0.9
         self._assert_consistency(F.deemphasis, (waveform, coeff))
+
+    def test_freq_ir(self):
+        mags = torch.tensor([0, 0.5, 1.0], device=self.device, dtype=self.dtype)
+        self._assert_consistency(F.frequency_impulse_response, (mags,))

torchaudio/prototype/functional/__init__.py

@@ -1,6 +1,7 @@
-from ._dsp import adsr_envelope, extend_pitch, oscillator_bank, sinc_impulse_response
+from ._dsp import adsr_envelope, extend_pitch, frequency_impulse_response, oscillator_bank, sinc_impulse_response
 from .functional import add_noise, barkscale_fbanks, convolve, deemphasis, fftconvolve, preemphasis, speed
 
+
 __all__ = [
     "add_noise",
     "adsr_envelope",
@@ -9,6 +10,7 @@
     "deemphasis",
     "extend_pitch",
     "fftconvolve",
+    "frequency_impulse_response",
     "oscillator_bank",
     "preemphasis",
     "sinc_impulse_response",

torchaudio/prototype/functional/_dsp.py

@@ -286,3 +286,23 @@ def sinc_impulse_response(cutoff: torch.Tensor, window_size: int = 513, high_pas
         filt = -filt
         filt[..., half] = 1.0 + filt[..., half]
     return filt
+
+
+def frequency_impulse_response(magnitudes):
+    """Create filter from desired frequency response
+
+    Args:
+        magnitudes: The desired frequency responses. Shape: `(..., num_fft_bins)`
+
+    Returns:
+        Tensor: Impulse response. Shape `(..., 2 * (num_fft_bins - 1))`
+    """
+    if magnitudes.min() < 0.0:
+        # Negative magnitude does not make sense but allowing so that autograd works
+        # around 0.
+        # Should we raise error?
+        warnings.warn("The input frequency response should not contain negative values.")
+    ir = torch.fft.fftshift(torch.fft.irfft(magnitudes), dim=-1)
+    device, dtype = magnitudes.device, magnitudes.dtype
+    window = torch.hann_window(ir.size(-1), periodic=False, device=device, dtype=dtype).expand_as(ir)
+    return ir * window