def audio_to_chroma(input_wavfile, hopsize, fx, norm=0):
"""Method for turning a wavefile into chroma features.
Parameters
----------
input_wavfile : str
Path to a wavefile.
hopsize : int
Number of samples between frames.
fx : function
Function that consumes 2D matrices of DFT coefficients and outputs
chroma features.
norm : scalar, default=0
Lp norm to apply to the features; skipped if not > 0.
Returns
-------
features : np.ndarray
Matrix of time-aligned chroma vectors, shaped (num_frames, 12).
"""
sigbuff = signal_buffer(input_wavfile, hopsize=hopsize)
pitch_spec = np.concatenate([CT.cqt_pool(batch)
for batch in sigbuff], axis=0)
features = fx(pitch_spec)
if norm > 0:
features = CT.lp_norm(features, norm)
return features
def generate_chroma_templates(num_qualities):
"""Generate chroma templates for some number of chord qualities.
The supported qualities are, in order:
[maj, min, maj7, min7, 7, maj6, min6, dim, aug, sus4, sus2, hdim7, dim7]
Parameters
----------
num_qualities : int
Number of chord qualities to generate chroma templates.
Returns
-------
templates : np.ndarray
Array of chroma templates, ordered by quality. The first 12 are Major,
the next 12 are minor, and so on.
"""
templates = []
position_idx = np.arange(12)
# For all qualities ...
for qual_idx in range(num_qualities):
quality = CT.QUALITIES[qual_idx]
# Translate the string into a bit-vector.
qual_array = np.array([int(v) for v in CT.QUALITY_MAP[quality]])
for root_idx in range(12):
# Rotate for all roots, C, C#, D ...
templates.append(qual_array[(position_idx - root_idx) % 12])
templates.append(np.ones(12))
return CT.lp_norm(np.array(templates), 1.0)
def generate_chroma_templates(num_qualities):
"""Generate chroma templates for some number of chord qualities.
The supported qualities are, in order:
[maj, min, maj7, min7, 7, maj6, min6, dim, aug, sus4, sus2, hdim7, dim7]
Parameters
----------
num_qualities : int
Number of chord qualities to generate chroma templates.
Returns
-------
templates : np.ndarray
Array of chroma templates, ordered by quality. The first 12 are Major,
the next 12 are minor, and so on.
"""
templates = []
position_idx = np.arange(12)
# For all qualities ...
for qual_idx in range(num_qualities):
quality = CT.QUALITIES[qual_idx]
# Translate the string into a bit-vector.
qual_array = np.array([int(v) for v in CT.QUALITY_MAP[quality]])
for root_idx in range(12):
# Rotate for all roots, C, C#, D ...
templates.append(qual_array[(position_idx - root_idx) % 12])
templates.append(np.ones(12))
return CT.lp_norm(np.array(templates), 1.0)
def audio_to_chroma(input_wavfile, hopsize, fx, norm=0):
"""Method for turning a wavefile into chroma features.
Parameters
----------
input_wavfile : str
Path to a wavefile.
hopsize : int
Number of samples between frames.
fx : function
Function that consumes 2D matrices of DFT coefficients and outputs
chroma features.
norm : scalar, default=0
Lp norm to apply to the features; skipped if not > 0.
Returns
-------
features : np.ndarray
Matrix of time-aligned chroma vectors, shaped (num_frames, 12).
"""
sigbuff = signal_buffer(input_wavfile, hopsize=hopsize)
pitch_spec = np.concatenate([CT.cqt_pool(batch) for batch in sigbuff],
axis=0)
features = fx(pitch_spec)
if norm > 0:
features = CT.lp_norm(features, norm)
return features
