added tests for dynamic tempo estimation

librosa/beat.py

@@ -330,7 +330,7 @@ def estimate_tempo(onset_envelope, sr=22050, hop_length=512, start_bpm=120,
 
 @cache(level=30)
 def tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120,
-          std_bpm=1.0, ac_size=4.0, aggregate=np.mean):
+          std_bpm=1.0, ac_size=4.0, max_tempo=320.0, aggregate=np.mean):
     """Estimate the tempo (beats per minute)
 
     Parameters
@@ -356,6 +356,9 @@ def tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120,
     ac_size : float > 0 [scalar]
         length (in seconds) of the auto-correlation window
 
+    max_tempo : float > 0 [scalar, optional]
+        If provided, only estimate tempo below this threshold
+
     aggregate : callable [optional]
         Aggregation function for estimating global tempo.
         If `None`, then tempo is estimated independently for each frame.
@@ -422,6 +425,12 @@ def tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120,
 
     # Weight the autocorrelation by a log-normal distribution
     prior = np.exp(-0.5 * ((np.log2(bpms) - np.log2(start_bpm)) / std_bpm)**2)
+
+    # Kill everything above the max tempo
+    if max_tempo is not None:
+        max_idx = np.argmax(bpms < max_tempo)
+        prior[:max_idx] = 0
+
     tg *= prior[:, np.newaxis]
 
     # Really, instead of multiplying by the prior, we should set up a

tests/test_beat.py

@@ -63,6 +63,34 @@ def __test(infile):
         yield (__test, infile)
 
 
+def test_tempo():
+
+    def __test(tempo, sr, hop_length, ac_size, aggregate, y):
+
+        tempo_est = librosa.beat.tempo(y=y, sr=sr, hop_length=hop_length,
+                                       ac_size=ac_size,
+                                       aggregate=aggregate)
+
+        # Being within 10% for the stable frames is close enough
+        if aggregate is None:
+            win_size = int(ac_size * sr // hop_length)
+            assert np.all(np.abs(tempo_est[win_size:-win_size] - tempo) <= 0.10 * tempo), (tempo,
+                    tempo_est[win_size:-win_size])
+        else:
+            assert np.abs(tempo_est - tempo) <= 0.10 * tempo, (tempo, tempo_est)
+
+    for sr in [22050, 44100]:
+        for tempo in [40, 60, 80, 110, 150, 160]:
+            # Make a pulse train at the target tempo
+            y = np.zeros(20 * sr)
+            delay = np.asscalar(librosa.time_to_samples(60./tempo, sr=sr))
+            y[::delay] = 1
+            for hop_length in [512, 1024]:
+                for ac_size in [4, 8]:
+                    for aggregate in [None, np.mean]:
+                        yield __test, tempo, sr, hop_length, ac_size, aggregate, y
+
+
 @raises(librosa.ParameterError)
 def test_beat_no_input():