diff --git a/ddsp/core.py b/ddsp/core.py
index 6b40721..0a6b95f 100644
--- a/ddsp/core.py
+++ b/ddsp/core.py
@@ -77,23 +77,53 @@ def scale_function(x):
     return 2 * torch.sigmoid(x)**(math.log(10)) + 1e-7
 
 
-def extract_loudness(signal, sampling_rate, block_size, n_fft=2048):
-    S = li.stft(
-        signal,
-        n_fft=n_fft,
-        hop_length=block_size,
-        win_length=n_fft,
-        center=True,
-    )
-    S = np.log(abs(S) + 1e-7)
-    f = li.fft_frequencies(sampling_rate, n_fft)
-    a_weight = li.A_weighting(f)
-
-    S = S + a_weight.reshape(-1, 1)
-
-    S = np.mean(S, 0)[..., :-1]
-
-    return S
+def amplitude_to_db(amplitude):
+    amin = 1e-20  # Avoid log(0) instabilities.
+    db = torch.log10(torch.clamp(amplitude, min=amin))
+    db *= 20.0
+    return db
+
+  
+def extract_loudness(audio, sampling_rate, block_size=None, n_fft=2048, frame_rate=None):
+    assert (block_size is None) != (frame_rate is None), "Specify exactly one of block_size or frame_rate"
+
+    if frame_rate is not None:
+        block_size = sample_rate // frame_rate
+    else:
+        frame_rate = int(sampling_rate / block_size)
+
+    if sampling_rate % frame_rate != 0:
+        raise ValueError(
+            'frame_rate: {} must evenly divide sample_rate: {}.'
+            'For default frame_rate: 250Hz, suggested sample_rate: 16kHz or 48kHz'
+            .format(frame_rate, sampling_rate))
+    
+    if isinstance(audio, np.ndarray):
+        audio = torch.tensor(audio)
+
+    # Temporarily a batch dimension for single examples.
+    is_1d = (len(audio.shape) == 1)
+    audio = audio[None, :] if is_1d else audio
+
+    # Take STFT.
+    overlap = 1 - block_size / n_fft
+    amplitude = torch.stft(audio, n_fft=n_fft, hop_length=block_size, center=True, pad_mode='reflect', return_complex=True).abs()
+
+    # Compute power.
+    power_db = amplitude_to_db(amplitude)
+
+    # Perceptual weighting.
+    frequencies = li.fft_frequencies(sr=sampling_rate, n_fft=n_fft)
+    a_weighting = li.A_weighting(frequencies)[None,:,None]
+    loudness = power_db + a_weighting
+
+    loudness = torch.mean(torch.pow(10, loudness / 10.0), axis=1)
+    loudness = 10.0 * torch.log10(torch.clamp(loudness, min=1e-20))
+
+    # Remove temporary batch dimension.
+    loudness = loudness[0] if is_1d else loudness
+
+    return loudness
 
 
 def extract_pitch(signal, sampling_rate, block_size):