def __init__(self, ledfx, config):
self._config = self.AUDIO_CONFIG_SCHEMA(config)
self._ledfx = ledfx
self._volume_filter = ExpFilter(np.zeros(1),
alpha_decay=0.01,
alpha_rise=0.1)
def _initialize_melbank(self):
"""Initialize all the melbank related variables"""
(self.mel_y, _, _) = self.compute_melmat()
self.mel_gain = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.01,
alpha_rise=0.99)
self.mel_smoothing = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.5,
alpha_rise=0.99)
self.common_filter = ExpFilter(alpha_decay=0.99, alpha_rise=0.01)
self.melbank_frequencies = np.linspace(
self._config['min_frequency'], self._config['max_frequency'],
self._config['samples']).astype(np.int32)
def create_filter(self, alpha_decay, alpha_rise):
# TODO: Since most effects reuse the same general filters it would be
# nice for all that computation to be shared. This mean that shared
# filters are needed, or if there is really just a small set of filters
# that those get added to the Melbank input source instead.
return ExpFilter(alpha_decay=alpha_decay, alpha_rise=alpha_rise)
class AudioInputSource(object):
_is_activated = False
_audio = None
_stream = None
_callbacks = []
_audioWindowSize = 4
_processed_audio_sample = None
_volume = -90
_volume_filter = ExpFilter(-90, alpha_decay=0.99, alpha_rise=0.99)
AUDIO_CONFIG_SCHEMA = vol.Schema(
{
vol.Optional("sample_rate", default=60): int,
vol.Optional("mic_rate", default=48000): int,
vol.Optional("fft_size", default=1024): int,
vol.Optional("device_index", default=0): int,
vol.Optional("pre_emphasis", default=0.3): float,
vol.Optional("min_volume", default=-70.0): float,
},
extra=vol.ALLOW_EXTRA,
)
def __init__(self, ledfx, config):
self._ledfx = ledfx
self.update_config(config)
def update_config(self, config):
"""Deactivate the audio, update the config, the reactivate"""
self.deactivate()
self._config = self.AUDIO_CONFIG_SCHEMA(config)
if len(self._callbacks) != 0:
self.activate()
def activate(self):
if self._audio is None:
self._audio = pyaudio.PyAudio()
# Setup a pre-emphasis filter to help balance the highs
self.pre_emphasis = None
if self._config["pre_emphasis"]:
self.pre_emphasis = aubio.digital_filter(3)
#
# old, do not use
# self.pre_emphasis.set_biquad(1., -self._config['pre_emphasis'], 0, 0, 0)
# USE THESE FOR SCOTT_MEL OR OTHERS
# self.pre_emphasis.set_biquad(1.3662, -1.9256, 0.5621, -1.9256, 0.9283)
# USE THESE FOR MATT_MEl
# weaker bass, good for vocals, highs
# self.pre_emphasis.set_biquad(0.87492, -1.74984, 0.87492, -1.74799, 0.75169)
# bass heavier overall more balanced
self.pre_emphasis.set_biquad(
0.85870, -1.71740, 0.85870, -1.71605, 0.71874
)
# Setup the phase vocoder to perform a windowed FFT
self._phase_vocoder = aubio.pvoc(
self._config["fft_size"],
self._config["mic_rate"] // self._config["sample_rate"],
)
self._frequency_domain_null = aubio.cvec(self._config["fft_size"])
self._frequency_domain = self._frequency_domain_null
self._frequency_domain_x = np.linspace(
0,
self._config["mic_rate"],
(self._config["fft_size"] // 2) + 1,
)
# Enumerate all of the input devices and find the one matching the
# configured device index
_LOGGER.info("Audio Input Devices:")
info = self._audio.get_host_api_info_by_index(0)
for i in range(0, info.get("deviceCount")):
if (
self._audio.get_device_info_by_host_api_device_index(0, i).get(
"maxInputChannels"
)
) > 0:
_LOGGER.info(
" [{}] {}".format(
i,
self._audio.get_device_info_by_host_api_device_index(
0, i
).get("name"),
)
)
# Open the audio stream and start processing the input
try:
self._stream = self._audio.open(
input_device_index=self._config["device_index"],
format=pyaudio.paFloat32,
channels=1,
rate=self._config["mic_rate"],
input=True,
frames_per_buffer=self._config["mic_rate"]
// self._config["sample_rate"],
stream_callback=self._audio_sample_callback,
)
self._stream.start_stream()
except OSError:
_LOGGER.critical("Unable to open Audio Device - please retry.")
self.deactivate
_LOGGER.info("Audio source opened.")
def deactivate(self):
if self._stream:
self._stream.stop_stream()
self._stream.close()
self._stream = None
self._rolling_window = None
_LOGGER.info("Audio source closed.")
def subscribe(self, callback):
"""Registers a callback with the input source"""
self._callbacks.append(callback)
if len(self._callbacks) == 1:
self.activate()
def unsubscribe(self, callback):
"""Unregisters a callback with the input source"""
if callback in self._callbacks:
self._callbacks.remove(callback)
if len(self._callbacks) == 0:
self.deactivate()
def _audio_sample_callback(self, in_data, frame_count, time_info, status):
"""Callback for when a new audio sample is acquired"""
self._raw_audio_sample = np.frombuffer(in_data, dtype=np.float32)
self.pre_process_audio()
self._invalidate_caches()
self._invoke_callbacks()
return (self._raw_audio_sample, pyaudio.paContinue)
def _invoke_callbacks(self):
"""Notifies all clients of the new data"""
for callback in self._callbacks:
callback()
def _invalidate_caches(self):
"""Invalidates the necessary cache"""
pass
def pre_process_audio(self):
"""
Pre-processing stage that will run on every sample, only
core functionality that will be used for every audio effect
should be done here. Everything else should be deferred until
queried by an effect.
"""
# Calculate the current volume for silence detection
self._volume = aubio.db_spl(self._raw_audio_sample)
if np.isinf(self._volume):
self._volume = 0.0
self._volume_filter.update(self._volume)
# Calculate the frequency domain from the filtered data and
# force all zeros when below the volume threshold
if self._volume_filter.value > self._config["min_volume"]:
self._processed_audio_sample = self._raw_audio_sample
# Perform a pre-emphasis to balance the highs and lows
if self.pre_emphasis:
self._processed_audio_sample = self.pre_emphasis(
self._raw_audio_sample
)
# Pass into the phase vocoder to get a windowed FFT
self._frequency_domain = self._phase_vocoder(
self._processed_audio_sample
)
else:
self._frequency_domain = self._frequency_domain_null
# Light up some notifications for developer mode
if self._ledfx.dev_enabled():
self._ledfx.events.fire_event(
GraphUpdateEvent(
"fft",
self._frequency_domain.norm,
self._frequency_domain_x,
)
)
def audio_sample(self, raw=False):
"""Returns the raw audio sample"""
if raw:
return self._raw_audio_sample
return self._processed_audio_sample
def frequency_domain(self):
return self._frequency_domain
def volume(self, filtered=True):
if filtered:
return self._volume_filter.value
return self._volume
def _initialize_melbank(self):
"""Initialize all the melbank related variables"""
# Few difference coefficient types for experimentation
if self._config["coeffs_type"] == "triangle":
melbank_mel = np.linspace(
aubio.hztomel(self._config["min_frequency"]),
aubio.hztomel(self._config["max_frequency"]),
self._config["samples"] + 2,
)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config["coeffs_type"] == "bark":
melbank_bark = np.linspace(
6.0 * np.arcsinh(self._config["min_frequency"] / 600.0),
6.0 * np.arcsinh(self._config["max_frequency"] / 600.0),
self._config["samples"] + 2,
)
self.melbank_frequencies = (
600.0 * np.sinh(melbank_bark / 6.0)
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
# Slaney coefficients will always produce 40 samples spanning 133Hz to
# 6000Hz
if self._config["coeffs_type"] == "slaney":
self.filterbank = aubio.filterbank(40, self._config["fft_size"])
self.filterbank.set_mel_coeffs_slaney(self._config["mic_rate"])
# Sanley frequencies are linear-log spaced where 133Hz to 1000Hz is linear
# spaced and 1000Hz to 6000Hz is log spaced. It also produced a hardcoded
# 40 samples.
lowestFrequency = 133.3
linearSpacing = 66.6666666
logSpacing = 1.0711703
linearFilters = 13
logFilters = 27
linearSpacedFreqs = (
lowestFrequency + np.arange(0, linearFilters) * linearSpacing
)
logSpacedFreqs = linearSpacedFreqs[-1] * np.power(
logSpacing, np.arange(1, logFilters + 1)
)
self._config["samples"] = 40
self.melbank_frequencies = np.hstack(
(linearSpacedFreqs, logSpacedFreqs)
).astype(np.float32)
# Standard mel coefficients
if self._config["coeffs_type"] == "mel":
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_mel_coeffs(
self._config["mic_rate"],
self._config["min_frequency"],
self._config["max_frequency"],
)
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config["min_frequency"]),
aubio.hztomel(self._config["max_frequency"]),
self._config["samples"],
)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]
)
# HTK mel coefficients
if self._config["coeffs_type"] == "htk":
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_mel_coeffs_htk(
self._config["mic_rate"],
self._config["min_frequency"],
self._config["max_frequency"],
)
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config["min_frequency"]),
aubio.hztomel(self._config["max_frequency"]),
self._config["samples"],
)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]
)
# Coefficients based on Scott's audio reactive led project
if self._config["coeffs_type"] == "scott":
(melmat, center_frequencies_hz, freqs,) = mel.compute_melmat(
num_mel_bands=self._config["samples"],
freq_min=self._config["min_frequency"],
freq_max=self._config["max_frequency"],
num_fft_bands=int(self._config["fft_size"] // 2) + 1,
sample_rate=self._config["mic_rate"],
)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
# "Mel"-spacing based on Scott's audio reactive led project. This
# should in theory be the same as the above, but there seems to be
# slight differences. Leaving both for science!
if self._config["coeffs_type"] == "scott_mel":
def hertz_to_scott(freq):
return 3340.0 * log(1 + (freq / 250.0), 9)
def scott_to_hertz(scott):
return 250.0 * (9 ** (scott / 3340.0)) - 250.0
melbank_scott = np.linspace(
hertz_to_scott(self._config["min_frequency"]),
hertz_to_scott(self._config["max_frequency"]),
self._config["samples"] + 2,
)
self.melbank_frequencies = np.array(
[scott_to_hertz(scott) for scott in melbank_scott]
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
# Modified scott_mel, spreads out the low range and compresses the
# highs
if self._config["coeffs_type"] == "matt_mel":
def hertz_to_matt(freq):
return 3700.0 * log(1 + (freq / 200.0), 13)
def matt_to_hertz(matt):
return 200.0 * (10 ** (matt / 3700.0)) - 200.0
melbank_matt = np.linspace(
hertz_to_matt(self._config["min_frequency"]),
hertz_to_matt(self._config["max_frequency"]),
self._config["samples"] + 2,
)
self.melbank_frequencies = np.array(
[matt_to_hertz(matt) for matt in melbank_matt]
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config["coeffs_type"] == "fixed":
ranges = FREQUENCY_RANGES.values()
upper_edges_hz = np.zeros(len(ranges))
lower_edges_hz = np.zeros(len(ranges))
for idx, value in enumerate(ranges):
lower_edges_hz[idx] = value.min
upper_edges_hz[idx] = value.max
(
melmat,
center_frequencies_hz,
freqs,
) = mel.compute_melmat_from_range(
lower_edges_hz=lower_edges_hz,
upper_edges_hz=upper_edges_hz,
num_fft_bands=int(self._config["fft_size"] // 2) + 1,
sample_rate=self._config["mic_rate"],
)
self._config["samples"] = len(center_frequencies_hz)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
if self._config["coeffs_type"] == "fixed_simple":
ranges = FREQUENCY_RANGES_SIMPLE.values()
upper_edges_hz = np.zeros(len(ranges))
lower_edges_hz = np.zeros(len(ranges))
for idx, value in enumerate(ranges):
lower_edges_hz[idx] = value.min
upper_edges_hz[idx] = value.max
(
melmat,
center_frequencies_hz,
freqs,
) = mel.compute_melmat_from_range(
lower_edges_hz=lower_edges_hz,
upper_edges_hz=upper_edges_hz,
num_fft_bands=int(self._config["fft_size"] // 2) + 1,
sample_rate=self._config["mic_rate"],
)
self._config["samples"] = len(center_frequencies_hz)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
self.melbank_frequencies = self.melbank_frequencies.astype(int)
# Normalize the filterbank triangles to a consistent height, the
# default coeffs (for types other than legacy) will be normalized
# by the triangles area which results in an uneven melbank
if (
self._config["coeffs_type"] != "scott"
and self._config["coeffs_type"] == "scott_mel"
):
coeffs = self.filterbank.get_coeffs()
coeffs /= np.max(coeffs, axis=-1)[:, None]
self.filterbank.set_coeffs(coeffs)
# Find the indexes for each of the frequency ranges
self.lows_index = self.mids_index = self.highs_index = 1
for i in range(0, len(self.melbank_frequencies)):
if (
self.melbank_frequencies[i]
< FREQUENCY_RANGES_SIMPLE["Low (1-250Hz)"].max
):
self.lows_index = i + 1
elif (
self.melbank_frequencies[i]
< FREQUENCY_RANGES_SIMPLE["Mid (250Hz-4kHz)"].max
):
self.mids_index = i + 1
elif (
self.melbank_frequencies[i]
< FREQUENCY_RANGES_SIMPLE["High (4kHz-24kHz)"].max
):
self.highs_index = i + 1
# Build up some of the common filters
self.mel_gain = ExpFilter(
np.tile(1e-1, self._config["samples"]),
alpha_decay=0.01,
alpha_rise=0.99,
)
self.mel_smoothing = ExpFilter(
np.tile(1e-1, self._config["samples"]),
alpha_decay=0.2,
alpha_rise=0.99,
)
self.common_filter = ExpFilter(alpha_decay=0.99, alpha_rise=0.01)
class MelbankInputSource(AudioInputSource):
CONFIG_SCHEMA = vol.Schema(
{
vol.Optional("samples", default=48): int,
vol.Optional("min_frequency", default=20): int,
vol.Optional("max_frequency", default=18000): int,
vol.Optional("min_volume", default=-70.0): float,
vol.Optional("pitch_tolerance", default=0.8): float,
vol.Optional("min_volume_count", default=20): int,
vol.Optional("power", default=1.0): float,
vol.Optional("coeffs_type", default="matt_mel"): str,
},
extra=vol.ALLOW_EXTRA,
)
def __init__(self, ledfx, config):
config = self.CONFIG_SCHEMA(config)
super().__init__(ledfx, config)
self._initialize_melbank()
self._initialize_pitch()
self._initialize_tempo()
self._initialize_onset()
self._initialize_oscillator()
def update_config(self, config):
validated_config = self.CONFIG_SCHEMA(config)
super().update_config(validated_config)
self._initialize_melbank()
self._initialize_pitch()
self._initialize_tempo()
self._initialize_onset()
self._initialize_oscillator()
def _invalidate_caches(self):
"""Invalidates the cache for all melbank related data"""
super()._invalidate_caches()
self.onset.cache_clear()
self.oscillator.cache_clear()
self.melbank.cache_clear()
self.melbank_filtered.cache_clear()
self.interpolated_melbank.cache_clear()
self.midi_value.cache_clear()
def _initialize_pitch(self):
self.pitch_o = aubio.pitch(
"schmitt",
self._config["fft_size"],
self._config["mic_rate"] // self._config["sample_rate"],
self._config["mic_rate"],
)
self.pitch_o.set_unit("midi")
self.pitch_o.set_tolerance(self._config["pitch_tolerance"])
def _initialize_tempo(self):
self.tempo_o = aubio.tempo(
"default",
self._config["fft_size"],
self._config["mic_rate"] // self._config["sample_rate"],
self._config["mic_rate"],
)
def _initialize_onset(self):
self.onset_high = aubio.onset(
"specflux",
self._config["fft_size"],
self._config["mic_rate"] // self._config["sample_rate"],
self._config["mic_rate"],
)
self.onset_soft = aubio.onset(
"phase",
self._config["fft_size"],
self._config["mic_rate"] // self._config["sample_rate"],
self._config["mic_rate"],
)
self.onset_mids = aubio.onset(
"specdiff",
self._config["fft_size"],
self._config["mic_rate"] // self._config["sample_rate"],
self._config["mic_rate"],
)
def _initialize_oscillator(self):
self.beat_timestamp = time.time()
self.beat_period = 2
def _initialize_melbank(self):
"""Initialize all the melbank related variables"""
# Few difference coefficient types for experimentation
if self._config["coeffs_type"] == "triangle":
melbank_mel = np.linspace(
aubio.hztomel(self._config["min_frequency"]),
aubio.hztomel(self._config["max_frequency"]),
self._config["samples"] + 2,
)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config["coeffs_type"] == "bark":
melbank_bark = np.linspace(
6.0 * np.arcsinh(self._config["min_frequency"] / 600.0),
6.0 * np.arcsinh(self._config["max_frequency"] / 600.0),
self._config["samples"] + 2,
)
self.melbank_frequencies = (
600.0 * np.sinh(melbank_bark / 6.0)
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
# Slaney coefficients will always produce 40 samples spanning 133Hz to
# 6000Hz
if self._config["coeffs_type"] == "slaney":
self.filterbank = aubio.filterbank(40, self._config["fft_size"])
self.filterbank.set_mel_coeffs_slaney(self._config["mic_rate"])
# Sanley frequencies are linear-log spaced where 133Hz to 1000Hz is linear
# spaced and 1000Hz to 6000Hz is log spaced. It also produced a hardcoded
# 40 samples.
lowestFrequency = 133.3
linearSpacing = 66.6666666
logSpacing = 1.0711703
linearFilters = 13
logFilters = 27
linearSpacedFreqs = (
lowestFrequency + np.arange(0, linearFilters) * linearSpacing
)
logSpacedFreqs = linearSpacedFreqs[-1] * np.power(
logSpacing, np.arange(1, logFilters + 1)
)
self._config["samples"] = 40
self.melbank_frequencies = np.hstack(
(linearSpacedFreqs, logSpacedFreqs)
).astype(np.float32)
# Standard mel coefficients
if self._config["coeffs_type"] == "mel":
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_mel_coeffs(
self._config["mic_rate"],
self._config["min_frequency"],
self._config["max_frequency"],
)
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config["min_frequency"]),
aubio.hztomel(self._config["max_frequency"]),
self._config["samples"],
)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]
)
# HTK mel coefficients
if self._config["coeffs_type"] == "htk":
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_mel_coeffs_htk(
self._config["mic_rate"],
self._config["min_frequency"],
self._config["max_frequency"],
)
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config["min_frequency"]),
aubio.hztomel(self._config["max_frequency"]),
self._config["samples"],
)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]
)
# Coefficients based on Scott's audio reactive led project
if self._config["coeffs_type"] == "scott":
(melmat, center_frequencies_hz, freqs,) = mel.compute_melmat(
num_mel_bands=self._config["samples"],
freq_min=self._config["min_frequency"],
freq_max=self._config["max_frequency"],
num_fft_bands=int(self._config["fft_size"] // 2) + 1,
sample_rate=self._config["mic_rate"],
)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
# "Mel"-spacing based on Scott's audio reactive led project. This
# should in theory be the same as the above, but there seems to be
# slight differences. Leaving both for science!
if self._config["coeffs_type"] == "scott_mel":
def hertz_to_scott(freq):
return 3340.0 * log(1 + (freq / 250.0), 9)
def scott_to_hertz(scott):
return 250.0 * (9 ** (scott / 3340.0)) - 250.0
melbank_scott = np.linspace(
hertz_to_scott(self._config["min_frequency"]),
hertz_to_scott(self._config["max_frequency"]),
self._config["samples"] + 2,
)
self.melbank_frequencies = np.array(
[scott_to_hertz(scott) for scott in melbank_scott]
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
# Modified scott_mel, spreads out the low range and compresses the
# highs
if self._config["coeffs_type"] == "matt_mel":
def hertz_to_matt(freq):
return 3700.0 * log(1 + (freq / 200.0), 13)
def matt_to_hertz(matt):
return 200.0 * (10 ** (matt / 3700.0)) - 200.0
melbank_matt = np.linspace(
hertz_to_matt(self._config["min_frequency"]),
hertz_to_matt(self._config["max_frequency"]),
self._config["samples"] + 2,
)
self.melbank_frequencies = np.array(
[matt_to_hertz(matt) for matt in melbank_matt]
).astype(np.float32)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_triangle_bands(
self.melbank_frequencies, self._config["mic_rate"]
)
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config["coeffs_type"] == "fixed":
ranges = FREQUENCY_RANGES.values()
upper_edges_hz = np.zeros(len(ranges))
lower_edges_hz = np.zeros(len(ranges))
for idx, value in enumerate(ranges):
lower_edges_hz[idx] = value.min
upper_edges_hz[idx] = value.max
(
melmat,
center_frequencies_hz,
freqs,
) = mel.compute_melmat_from_range(
lower_edges_hz=lower_edges_hz,
upper_edges_hz=upper_edges_hz,
num_fft_bands=int(self._config["fft_size"] // 2) + 1,
sample_rate=self._config["mic_rate"],
)
self._config["samples"] = len(center_frequencies_hz)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
if self._config["coeffs_type"] == "fixed_simple":
ranges = FREQUENCY_RANGES_SIMPLE.values()
upper_edges_hz = np.zeros(len(ranges))
lower_edges_hz = np.zeros(len(ranges))
for idx, value in enumerate(ranges):
lower_edges_hz[idx] = value.min
upper_edges_hz[idx] = value.max
(
melmat,
center_frequencies_hz,
freqs,
) = mel.compute_melmat_from_range(
lower_edges_hz=lower_edges_hz,
upper_edges_hz=upper_edges_hz,
num_fft_bands=int(self._config["fft_size"] // 2) + 1,
sample_rate=self._config["mic_rate"],
)
self._config["samples"] = len(center_frequencies_hz)
self.filterbank = aubio.filterbank(
self._config["samples"], self._config["fft_size"]
)
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
self.melbank_frequencies = self.melbank_frequencies.astype(int)
# Normalize the filterbank triangles to a consistent height, the
# default coeffs (for types other than legacy) will be normalized
# by the triangles area which results in an uneven melbank
if (
self._config["coeffs_type"] != "scott"
and self._config["coeffs_type"] == "scott_mel"
):
coeffs = self.filterbank.get_coeffs()
coeffs /= np.max(coeffs, axis=-1)[:, None]
self.filterbank.set_coeffs(coeffs)
# Find the indexes for each of the frequency ranges
self.lows_index = self.mids_index = self.highs_index = 1
for i in range(0, len(self.melbank_frequencies)):
if (
self.melbank_frequencies[i]
< FREQUENCY_RANGES_SIMPLE["Low (1-250Hz)"].max
):
self.lows_index = i + 1
elif (
self.melbank_frequencies[i]
< FREQUENCY_RANGES_SIMPLE["Mid (250Hz-4kHz)"].max
):
self.mids_index = i + 1
elif (
self.melbank_frequencies[i]
< FREQUENCY_RANGES_SIMPLE["High (4kHz-24kHz)"].max
):
self.highs_index = i + 1
# Build up some of the common filters
self.mel_gain = ExpFilter(
np.tile(1e-1, self._config["samples"]),
alpha_decay=0.01,
alpha_rise=0.99,
)
self.mel_smoothing = ExpFilter(
np.tile(1e-1, self._config["samples"]),
alpha_decay=0.2,
alpha_rise=0.99,
)
self.common_filter = ExpFilter(alpha_decay=0.99, alpha_rise=0.01)
@lru_cache(maxsize=32)
def melbank(self):
"""Returns the raw melbank curve"""
if self.volume() > self._config["min_volume"]:
# Compute the filterbank from the frequency information
raw_filter_banks = self.filterbank(self.frequency_domain())
raw_filter_banks = raw_filter_banks ** 2.0
self.mel_gain.update(np.max(smooth(raw_filter_banks, sigma=1.0)))
filter_banks = raw_filter_banks / self.mel_gain.value
filter_banks = self.mel_smoothing.update(filter_banks)
else:
raw_filter_banks = np.zeros(self._config["samples"])
filter_banks = raw_filter_banks
if self._ledfx.dev_enabled():
self._ledfx.events.fire_event(
GraphUpdateEvent(
"raw",
raw_filter_banks,
np.array(self.melbank_frequencies),
)
)
self._ledfx.events.fire_event(
GraphUpdateEvent(
"melbank",
filter_banks,
np.array(self.melbank_frequencies),
)
)
return filter_banks
def melbank_lows(self):
return self.melbank()[: self.lows_index]
def melbank_mids(self):
return self.melbank()[self.lows_index : self.mids_index]
def melbank_highs(self):
return self.melbank()[self.mids_index :]
@lru_cache(maxsize=32)
def melbank_filtered(self):
# TODO: Should probably account for the filtered melbank not being
# queried every frame which would result in a poor filter. Need a
# good balance between wasting compute resources and quality filters.
return self.common_filter.update(self.melbank())
def sample_melbank(self, hz):
"""Samples the melbank curve at a given frequency"""
return np.interp(hz, self.melbank_frequencies, self.melbank())
@lru_cache(maxsize=32)
def interpolated_melbank(self, size, filtered=True):
"""Returns a melbank curve interpolated up to a given size"""
if filtered is True:
return math.interpolate(self.melbank_filtered(), size)
return math.interpolate(self.melbank(), size)
@lru_cache(maxsize=32)
def midi_value(self):
# If pyaudio is returning null, then we just return 0 for midi_value and wait for the device starts sending audio.
try:
return self.pitch_o(self.audio_sample())[0]
except ValueError:
return 0
@lru_cache(maxsize=32)
def onset(self):
return {
"mids": bool(self.onset_mids(self.audio_sample(raw=True))[0]),
"soft": bool(self.onset_soft(self.audio_sample(raw=True))[0]),
"high": bool(self.onset_high(self.audio_sample(raw=True))[0]),
}
@lru_cache(maxsize=32)
def oscillator(self):
"""
returns a float (0<=x<1) corresponding to the current position of beat tracker.
this is synced and quantized to the bpm of whatever is playing.
0 0.5 <1
{----------time for one beat---------}
^ --> --> -->
value of
oscillator
"""
# update tempo and oscillator
is_beat = bool(self.tempo_o(self.audio_sample(raw=True))[0])
if is_beat:
self.beat_period = self.tempo_o.get_period_s()
self.beat_timestamp = time.time()
oscillator = 0
else:
time_since_beat = time.time() - self.beat_timestamp
oscillator = (
1 - (self.beat_period - time_since_beat) / self.beat_period
)
# ensure it's between 0 and 1. useful when audio cuts
oscillator = min(1, oscillator)
oscillator = max(0, oscillator)
return oscillator, is_beat
class AudioInputSource(object):
_audio = None
_stream = None
_callbacks = []
_audioWindowSize = 4
AUDIO_CONFIG_SCHEMA = vol.Schema(
{
vol.Optional('sample_rate', default=60): int,
vol.Optional('mic_rate', default=48000): int,
vol.Optional('fft_size', default=1024): int,
vol.Optional('device_index', default=0): int,
vol.Optional('pre_emphasis', default=0.0): float,
vol.Optional('min_volume', default=-70.0): float
},
extra=vol.ALLOW_EXTRA)
def __init__(self, ledfx, config):
self._config = self.AUDIO_CONFIG_SCHEMA(config)
self._ledfx = ledfx
self._volume = -90
self._volume_filter = ExpFilter(-90, alpha_decay=0.01, alpha_rise=0.99)
def activate(self):
if self._audio is None:
self._audio = pyaudio.PyAudio()
# Setup a pre-emphasis filter to help balance the highs
self.pre_emphasis = None
if self._config['pre_emphasis']:
self.pre_emphasis = aubio.digital_filter(3)
self.pre_emphasis.set_biquad(1., -self._config['pre_emphasis'], 0,
0, 0)
# Setup the phase vocoder to perform a windowed FFT
self._phase_vocoder = aubio.pvoc(
self._config['fft_size'],
self._config['mic_rate'] // self._config['sample_rate'])
self._frequency_domain_null = aubio.cvec(self._config['fft_size'])
self._frequency_domain = self._frequency_domain_null
self._frequency_domain_x = np.linspace(
0, self._config['mic_rate'], (self._config["fft_size"] // 2) + 1)
# Enumerate all of the input devices and find the one matching the
# configured device index
_LOGGER.info("Audio Input Devices:")
info = self._audio.get_host_api_info_by_index(0)
for i in range(0, info.get('deviceCount')):
if (self._audio.get_device_info_by_host_api_device_index(
0, i).get('maxInputChannels')) > 0:
_LOGGER.info(" [{}] {}".format(
i,
self._audio.get_device_info_by_host_api_device_index(
0, i).get('name')))
# Open the audio stream and start processing the input
self._stream = self._audio.open(
#input_device_index=self._config['device_index'],
format=pyaudio.paFloat32,
channels=1,
rate=self._config['mic_rate'],
input=True,
frames_per_buffer=self._config['mic_rate'] //
self._config['sample_rate'],
stream_callback=self._audio_sample_callback)
self._stream.start_stream()
_LOGGER.info("Audio source opened.")
def deactivate(self):
self._stream.stop_stream()
self._stream.close()
self._stream = None
self._rolling_window = None
_LOGGER.info("Audio source closed.")
def subscribe(self, callback):
"""Registers a callback with the input source"""
self._callbacks.append(callback)
if len(self._callbacks) == 1:
self.activate()
def unsubscribe(self, callback):
"""Unregisters a callback with the input source"""
if callback in self._callbacks:
self._callbacks.remove(callback)
if len(self._callbacks) == 0:
self.deactivate()
def _audio_sample_callback(self, in_data, frame_count, time_info, status):
"""Callback for when a new audio sample is acquired"""
self._raw_audio_sample = np.fromstring(in_data, dtype=np.float32)
self.pre_process_audio()
self._invalidate_caches()
self._invoke_callbacks()
return (self._raw_audio_sample, pyaudio.paContinue)
def _invoke_callbacks(self):
"""Notifies all clients of the new data"""
for callback in self._callbacks:
callback()
def _invalidate_caches(self):
"""Invalidates the necessary cache"""
pass
def pre_process_audio(self):
"""
Pre-processing stage that will run on every sample, only
core functionality that will be used for every audio effect
should be done here. Everything else should be deferred until
queried by an effect.
"""
# Calculate the current volume for silence detection
self._volume = aubio.db_spl(self._raw_audio_sample)
self._volume_filter.update(self._volume)
# Calculate the frequency domain from the filtered data and
# force all zeros when below the volume threshold
if self._volume_filter.value > self._config['min_volume']:
self._processed_audio_sample = self._raw_audio_sample
# Perform a pre-emphasis to balance the highs and lows
if self.pre_emphasis:
self._processed_audio_sample = self.pre_emphasis(
self._raw_audio_sample)
# Pass into the phase vocoder to get a windowed FFT
self._frequency_domain = self._phase_vocoder(
self._processed_audio_sample)
else:
self._frequency_domain = self._frequency_domain_null
# Light up some notifications for developer mode
if self._ledfx.dev_enabled():
self._ledfx.events.fire_event(
GraphUpdateEvent('fft', self._frequency_domain.norm,
self._frequency_domain_x))
def audio_sample(self, raw=False):
"""Returns the raw audio sample"""
if raw:
return self._raw_audio_sample
return self._processed_audio_sample
def frequency_domain(self):
return self._frequency_domain
def volume(self, filtered=True):
if filtered:
return self._volume_filter.value
return self._volume
def _initialize_melbank(self):
"""Initialize all the melbank related variables"""
# Few difference coefficient types for experimentation
if self._config['coeffs_type'] == 'triangle':
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'] + 2)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]).astype(np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config['coeffs_type'] == 'bark':
melbank_bark = np.linspace(
6.0 * np.arcsinh(self._config['min_frequency'] / 600.0),
6.0 * np.arcsinh(self._config['max_frequency'] / 600.0),
self._config['samples'] + 2)
self.melbank_frequencies = (600.0 *
np.sinh(melbank_bark / 6.0)).astype(
np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
# Slaney coefficients will always produce 40 samples spanning 133Hz to 6000Hz
if self._config['coeffs_type'] == 'slaney':
self.filterbank = aubio.filterbank(40, self._config['fft_size'])
self.filterbank.set_mel_coeffs_slaney(self._config['mic_rate'])
# Sanley frequencies are linear-log spaced where 133Hz to 1000Hz is linear
# spaced and 1000Hz to 6000Hz is log spaced. It also produced a hardcoded
# 40 samples.
lowestFrequency = 133.3
linearSpacing = 66.6666666
logSpacing = 1.0711703
linearFilters = 13
logFilters = 27
linearSpacedFreqs = lowestFrequency + np.arange(
0, linearFilters) * linearSpacing
logSpacedFreqs = linearSpacedFreqs[-1] * np.power(
logSpacing, np.arange(1, logFilters + 1))
self._config['samples'] = 40
self.melbank_frequencies = np.hstack(
(linearSpacedFreqs, logSpacedFreqs)).astype(np.float32)
# Standard mel coefficients
if self._config['coeffs_type'] == 'mel':
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_mel_coeffs(self._config['mic_rate'],
self._config['min_frequency'],
self._config['max_frequency'])
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'])
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel])
# HTK mel coefficients
if self._config['coeffs_type'] == 'htk':
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_mel_coeffs_htk(self._config['mic_rate'],
self._config['min_frequency'],
self._config['max_frequency'])
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'])
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel])
# Coefficients based on Scott's audio reactive led project
if self._config['coeffs_type'] == 'scott':
(melmat, center_frequencies_hz, freqs) = mel.compute_melmat(
num_mel_bands=self._config['samples'],
freq_min=self._config['min_frequency'],
freq_max=self._config['max_frequency'],
num_fft_bands=int(self._config['fft_size'] // 2) + 1,
sample_rate=self._config['mic_rate'])
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
# "Mel"-spacing based on Scott's audio reactive led project. This
# should in theory be the same as the above, but there seems to be
# slight differences. Leaving both for science!
if self._config['coeffs_type'] == 'scott_mel':
def hertz_to_scott(freq):
return 3340.0 * log(1 + (freq / 250.0), 9)
def scott_to_hertz(scott):
return 250.0 * (9**(scott / 3340.0)) - 250.0
melbank_scott = np.linspace(
hertz_to_scott(self._config['min_frequency']),
hertz_to_scott(self._config['max_frequency']),
self._config['samples'] + 2)
self.melbank_frequencies = np.array([
scott_to_hertz(scott) for scott in melbank_scott
]).astype(np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
self.melbank_frequencies = self.melbank_frequencies.astype(int)
# Normalize the filterbank triangles to a consistent height, the
# default coeffs (for types other than legacy) will be normalized
# by the triangles area which results in an uneven melbank
if self._config['coeffs_type'] != 'scott' and self._config[
'coeffs_type'] == 'scott_mel':
coeffs = self.filterbank.get_coeffs()
coeffs /= np.max(coeffs, axis=-1)[:, None]
self.filterbank.set_coeffs(coeffs)
# Find the indexes for each of the frequency ranges
for i in range(0, len(self.melbank_frequencies) - 1):
if self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE['low'].max:
self.lows_index = i
elif self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE[
'mid'].max:
self.mids_index = i
elif self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE[
'high'].max:
self.highs_index = i
# Build up some of the common filters
self.mel_gain = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.01,
alpha_rise=0.99)
self.mel_smoothing = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.2,
alpha_rise=0.99)
self.common_filter = ExpFilter(alpha_decay=0.99, alpha_rise=0.01)
class MelbankInputSource(AudioInputSource):
CONFIG_SCHEMA = vol.Schema(
{
vol.Optional('samples', default=24): int,
vol.Optional('min_frequency', default=20): int,
vol.Optional('max_frequency', default=18000): int,
vol.Optional('min_volume', default=-70.0): float,
vol.Optional('min_volume_count', default=20): int,
vol.Optional('coeffs_type', default="scott"): str
},
extra=vol.ALLOW_EXTRA)
def __init__(self, ledfx, config):
config = self.CONFIG_SCHEMA(config)
super().__init__(ledfx, config)
self._initialize_melbank()
def _invalidate_caches(self):
"""Invalidates the cache for all melbank related data"""
super()._invalidate_caches()
self.melbank.cache_clear()
self.melbank_filtered.cache_clear()
self.interpolated_melbank.cache_clear()
def _initialize_melbank(self):
"""Initialize all the melbank related variables"""
# Few difference coefficient types for experimentation
if self._config['coeffs_type'] == 'triangle':
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'] + 2)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]).astype(np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config['coeffs_type'] == 'bark':
melbank_bark = np.linspace(
6.0 * np.arcsinh(self._config['min_frequency'] / 600.0),
6.0 * np.arcsinh(self._config['max_frequency'] / 600.0),
self._config['samples'] + 2)
self.melbank_frequencies = (600.0 *
np.sinh(melbank_bark / 6.0)).astype(
np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
# Slaney coefficients will always produce 40 samples spanning 133Hz to 6000Hz
if self._config['coeffs_type'] == 'slaney':
self.filterbank = aubio.filterbank(40, self._config['fft_size'])
self.filterbank.set_mel_coeffs_slaney(self._config['mic_rate'])
# Sanley frequencies are linear-log spaced where 133Hz to 1000Hz is linear
# spaced and 1000Hz to 6000Hz is log spaced. It also produced a hardcoded
# 40 samples.
lowestFrequency = 133.3
linearSpacing = 66.6666666
logSpacing = 1.0711703
linearFilters = 13
logFilters = 27
linearSpacedFreqs = lowestFrequency + np.arange(
0, linearFilters) * linearSpacing
logSpacedFreqs = linearSpacedFreqs[-1] * np.power(
logSpacing, np.arange(1, logFilters + 1))
self._config['samples'] = 40
self.melbank_frequencies = np.hstack(
(linearSpacedFreqs, logSpacedFreqs)).astype(np.float32)
# Standard mel coefficients
if self._config['coeffs_type'] == 'mel':
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_mel_coeffs(self._config['mic_rate'],
self._config['min_frequency'],
self._config['max_frequency'])
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'])
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel])
# HTK mel coefficients
if self._config['coeffs_type'] == 'htk':
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_mel_coeffs_htk(self._config['mic_rate'],
self._config['min_frequency'],
self._config['max_frequency'])
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'])
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel])
# Coefficients based on Scott's audio reactive led project
if self._config['coeffs_type'] == 'scott':
(melmat, center_frequencies_hz, freqs) = mel.compute_melmat(
num_mel_bands=self._config['samples'],
freq_min=self._config['min_frequency'],
freq_max=self._config['max_frequency'],
num_fft_bands=int(self._config['fft_size'] // 2) + 1,
sample_rate=self._config['mic_rate'])
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
# "Mel"-spacing based on Scott's audio reactive led project. This
# should in theory be the same as the above, but there seems to be
# slight differences. Leaving both for science!
if self._config['coeffs_type'] == 'scott_mel':
def hertz_to_scott(freq):
return 3340.0 * log(1 + (freq / 250.0), 9)
def scott_to_hertz(scott):
return 250.0 * (9**(scott / 3340.0)) - 250.0
melbank_scott = np.linspace(
hertz_to_scott(self._config['min_frequency']),
hertz_to_scott(self._config['max_frequency']),
self._config['samples'] + 2)
self.melbank_frequencies = np.array([
scott_to_hertz(scott) for scott in melbank_scott
]).astype(np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
self.melbank_frequencies = self.melbank_frequencies.astype(int)
# Normalize the filterbank triangles to a consistent height, the
# default coeffs (for types other than legacy) will be normalized
# by the triangles area which results in an uneven melbank
if self._config['coeffs_type'] != 'scott' and self._config[
'coeffs_type'] == 'scott_mel':
coeffs = self.filterbank.get_coeffs()
coeffs /= np.max(coeffs, axis=-1)[:, None]
self.filterbank.set_coeffs(coeffs)
# Find the indexes for each of the frequency ranges
for i in range(0, len(self.melbank_frequencies) - 1):
if self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE['low'].max:
self.lows_index = i
elif self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE[
'mid'].max:
self.mids_index = i
elif self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE[
'high'].max:
self.highs_index = i
# Build up some of the common filters
self.mel_gain = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.01,
alpha_rise=0.99)
self.mel_smoothing = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.2,
alpha_rise=0.99)
self.common_filter = ExpFilter(alpha_decay=0.99, alpha_rise=0.01)
@lru_cache(maxsize=32)
def melbank(self):
"""Returns the raw melbank curve"""
if self.volume() > self._config['min_volume']:
# Compute the filterbank from the frequency information
raw_filter_banks = self.filterbank(self.frequency_domain())
raw_filter_banks = raw_filter_banks**2.0
self.mel_gain.update(np.max(smooth(raw_filter_banks, sigma=1.0)))
filter_banks = raw_filter_banks / self.mel_gain.value
filter_banks = self.mel_smoothing.update(filter_banks)
else:
raw_filter_banks = np.zeros(self._config['samples'])
filter_banks = raw_filter_banks
if self._ledfx.dev_enabled():
self._ledfx.events.fire_event(
GraphUpdateEvent('raw', raw_filter_banks,
np.array(self.melbank_frequencies)))
self._ledfx.events.fire_event(
GraphUpdateEvent('melbank', filter_banks,
np.array(self.melbank_frequencies)))
return filter_banks
def melbank_lows(self):
return self.melbank()[:self.lows_index]
def melbank_mids(self):
return self.melbank()[self.lows_index + 1:self.mids_index]
def melbank_highs(self):
return self.melbank()[self.highs_index:]
@lru_cache(maxsize=32)
def melbank_filtered(self):
# TODO: Should probably account for the filtered melbank not being
# queried every frame which would result in a poor filter. Need a
# good balance between wasting compute resources and quality filters.
return self.common_filter.update(self.melbank())
def sample_melbank(self, hz):
"""Samples the melbank curve at a given frequency"""
return np.interp(hz, self.melbank_frequencies, self.melbank())
@lru_cache(maxsize=32)
def interpolated_melbank(self, size, filtered=True):
"""Returns a melbank curve interpolated up to a given size"""
if filtered is True:
return math.interpolate(self.melbank_filtered(), size)
return math.interpolate(self.melbank(), size)
class AudioInputSource(object):
_audio = None
_stream = None
_callbacks = []
_audioWindowSize = 4
AUDIO_CONFIG_SCHEMA = vol.Schema(
{
vol.Optional('sample_rate', default=60): int,
vol.Optional('mic_rate', default=44100): int,
vol.Optional('window_size', default=4): int
},
extra=vol.ALLOW_EXTRA)
def __init__(self, ledfx, config):
self._config = self.AUDIO_CONFIG_SCHEMA(config)
self._ledfx = ledfx
self._volume_filter = ExpFilter(np.zeros(1),
alpha_decay=0.01,
alpha_rise=0.1)
def activate(self):
if self._audio is None:
self._audio = pyaudio.PyAudio()
frames_per_buffer = int(self._config['mic_rate'] /
self._config['sample_rate'])
self._rolling_window = np.random.rand(self._config['window_size'],
frames_per_buffer) / 1e16
self._hamming_window = np.hamming(frames_per_buffer)
self._stream = self._audio.open(
format=pyaudio.paInt16,
channels=1,
rate=self._config['mic_rate'],
input=True,
frames_per_buffer=frames_per_buffer,
stream_callback=self._audio_sample_callback)
self._stream.start_stream()
_LOGGER.info("Audio source opened.")
def deactivate(self):
self._stream.stop_stream()
_LOGGER.info("Audio source closed. 1")
self._stream.close()
_LOGGER.info("Audio source closed.2")
self._stream = None
self._rolling_window = None
_LOGGER.info("Audio source closed.")
def subscribe(self, callback):
"""Registers a callback with the input source"""
self._callbacks.append(callback)
if len(self._callbacks) == 1:
self.activate()
def unsubscribe(self, callback):
"""Unregisters a callback with the input srouce"""
self._callbacks.remove(callback)
if len(self._callbacks) == 0:
self.deactivate()
def _audio_sample_callback(self, in_data, frame_count, time_info, status):
"""Callback for when a new audio sample is acquired"""
self._rolling_window[:-1] = self._rolling_window[1:]
self._rolling_window[-1, :] = np.fromstring(in_data, dtype=np.int16)
self._invalidate_caches()
self._invoke_callbacks()
return (self._rolling_window[:-1], pyaudio.paContinue)
def _invoke_callbacks(self):
"""Notifies all clients of the new data"""
for callback in self._callbacks:
callback()
def _invalidate_caches(self):
"""Invalidates the necessary cache"""
self.volume.cache_clear()
def audio_sample(self,
rolling_window=True,
apply_hamming=True,
pre_emphasis=0.97):
"""Returns the raw audio sample"""
sample = self._rolling_window
if not rolling_window:
sample = self._rolling_window[-1, :]
# TODO: This was updated to perform the hamming window frame by frame.
# need to evaluate how this impacts the melbank.
if apply_hamming:
sample = sample * self._hamming_window
# TODO: Added a pre-emphasis which seems to help amplify the higher
# frequencies giving a more balanaced melbank. Need to evaluate how
# this fully impacts the effects.
if pre_emphasis:
sample = np.append(np.atleast_2d(sample[0]),
sample[1:] - pre_emphasis * sample[:-1],
axis=0)
return sample
@lru_cache(maxsize=32)
def volume(self, filtered=True):
if filtered:
return self._volume_filter.update(self.volume(filtered=False))
return np.abs(
np.max(self._rolling_window) -
np.min(self._rolling_window)) / 2**16
class MelbankInputSource(AudioInputSource):
CONFIG_SCHEMA = vol.Schema(
{
vol.Optional('sample_rate', default=60): int,
vol.Optional('mic_rate', default=44100): int,
vol.Optional('window_size', default=4): int,
vol.Optional('samples', default=22): int,
vol.Optional('nfft', default=512): int,
vol.Optional('min_frequency', default=20): int,
vol.Optional('max_frequency', default=20000): int,
},
extra=vol.ALLOW_EXTRA)
def __init__(self, ledfx, config):
config = self.CONFIG_SCHEMA(config)
super().__init__(ledfx, config)
self._initialize_melbank()
def _invalidate_caches(self):
"""Invalidates the cache for all melbank related data"""
super()._invalidate_caches()
self.melbank.cache_clear()
self.interpolated_melbank.cache_clear()
def _initialize_melbank(self):
"""Initialize all the melbank related variables"""
(self.mel_y, _, _) = self.compute_melmat()
self.mel_gain = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.01,
alpha_rise=0.99)
self.mel_smoothing = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.5,
alpha_rise=0.99)
self.common_filter = ExpFilter(alpha_decay=0.99, alpha_rise=0.01)
self.melbank_frequencies = np.linspace(
self._config['min_frequency'], self._config['max_frequency'],
self._config['samples']).astype(np.int32)
def compute_melmat(self):
return mel.compute_melmat(
num_mel_bands=self._config['samples'],
freq_min=self._config['min_frequency'],
freq_max=self._config['max_frequency'],
num_fft_bands=int(self._config['nfft'] // 2) + 1,
sample_rate=self._config['mic_rate'])
@lru_cache(maxsize=32)
def melbank(self):
"""Returns the raw melbank curve"""
# Validate there is a substantial enough volume for processing
if self.volume() < 0.001:
filter_banks = np.zeros(self._config['samples'])
filter_banks = self.mel_smoothing.update(filter_banks)
return filter_banks
# Compress the audio data and convert it to a float
y_data = np.concatenate(self.audio_sample(), axis=0).astype(np.float32)
# Pad with zeros until the next power of two
N = len(y_data)
N_zeros = 2**int(np.ceil(np.log2(N))) - N
y_padded = np.pad(y_data, (0, N_zeros), mode='constant')
# Perform the FFT to get the magnitudes
magnitude = np.abs(np.fft.rfft(y_padded, self._config['nfft']))
#power = ((1.0 / self._config['nfft']) * ((magnitude) ** 2))
# Compute the Mel filterbanks from the FFT data and scale
filter_banks = np.atleast_2d(magnitude).T * self.mel_y.T
filter_banks = np.sum(filter_banks, axis=0)
filter_banks = filter_banks**2.0
# Gain normalization
self.mel_gain.update(np.max(gaussian_filter1d(filter_banks,
sigma=1.0)))
filter_banks /= self.mel_gain.value
filter_banks = self.mel_smoothing.update(filter_banks)
# # TODO: Look into some better gain normalization as there seems to be some
# # issues with variable volume.
# self.mel_gain.update(np.mean(gaussian_filter1d(filter_banks, sigma=1.0)))
# #filter_banks -= (np.mean(filter_banks, axis=0) + 1e-8)
# filter_banks /= self.mel_gain.value
# filter_banks = self.mel_smoothing.update(filter_banks)
self._ledfx.events.fire_event(
MelbankUpdateEvent(filter_banks, self.melbank_frequencies))
return filter_banks
@lru_cache(maxsize=32)
def melbank_filtered(self):
# TODO: Should probably account for the filtered melbank not being
# queried every frame which would result in a poor filter. Need a
# good balance between wasting compute resources and quality filters.
return self.common_filter.update(self.melbank())
def sample_melbank(self, hz):
"""Samples the melbank curve at a given frequency"""
return np.interp(hz, self.melbank_frequencies, self.melbank())
@lru_cache(maxsize=32)
def interpolated_melbank(self, size, filtered=True):
"""Returns a melbank curve interpolated up to a given size"""
if filtered is True:
return math.interpolate(self.melbank_filtered(), size)
return math.interpolate(self.melbank(), size)
class MelbankInputSource(AudioInputSource):
CONFIG_SCHEMA = vol.Schema(
{
vol.Optional('samples', default=24): int,
vol.Optional('min_frequency', default=20): int,
vol.Optional('max_frequency', default=18000): int,
vol.Optional('min_volume', default=-70.0): float,
vol.Optional('pitch_tolerance', default=0.8): float,
vol.Optional('min_volume_count', default=20): int,
vol.Optional('coeffs_type', default="scott"): str
},
extra=vol.ALLOW_EXTRA)
def __init__(self, ledfx, config):
config = self.CONFIG_SCHEMA(config)
super().__init__(ledfx, config)
self._initialize_melbank()
self._initialize_pitch()
self._initialize_tempo()
self._initialize_onset()
self._initialize_oscillator()
def update_config(self, config):
validated_config = self.CONFIG_SCHEMA(config)
super().update_config(validated_config)
self._initialize_melbank()
self._initialize_pitch()
self._initialize_tempo()
self._initialize_onset()
self._initialize_oscillator()
def _invalidate_caches(self):
"""Invalidates the cache for all melbank related data"""
super()._invalidate_caches()
self.melbank.cache_clear()
self.melbank_filtered.cache_clear()
self.interpolated_melbank.cache_clear()
self.midi_value.cache_clear()
def _initialize_pitch(self):
self.pitch_o = aubio.pitch(
"schmitt", self._config['fft_size'],
self._config['mic_rate'] // self._config['sample_rate'],
self._config['mic_rate'])
self.pitch_o.set_unit("midi")
self.pitch_o.set_tolerance(self._config['pitch_tolerance'])
def _initialize_tempo(self):
self.tempo_o = aubio.tempo(
"default", self._config['fft_size'],
self._config['mic_rate'] // self._config['sample_rate'],
self._config['mic_rate'])
def _initialize_onset(self):
self.onset_high = aubio.onset(
"hfc", self._config['fft_size'],
self._config['mic_rate'] // self._config['sample_rate'],
self._config['mic_rate'])
self.onset_mids = aubio.onset(
"phase", self._config['fft_size'],
self._config['mic_rate'] // self._config['sample_rate'],
self._config['mic_rate'])
self.onset_lows = aubio.onset(
"specdiff", self._config['fft_size'],
self._config['mic_rate'] // self._config['sample_rate'],
self._config['mic_rate'])
def _initialize_oscillator(self):
self.beat_timestamp = time.time()
self.beat_period = 2
def _initialize_melbank(self):
"""Initialize all the melbank related variables"""
# Few difference coefficient types for experimentation
if self._config['coeffs_type'] == 'triangle':
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'] + 2)
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel]).astype(np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config['coeffs_type'] == 'bark':
melbank_bark = np.linspace(
6.0 * np.arcsinh(self._config['min_frequency'] / 600.0),
6.0 * np.arcsinh(self._config['max_frequency'] / 600.0),
self._config['samples'] + 2)
self.melbank_frequencies = (600.0 *
np.sinh(melbank_bark / 6.0)).astype(
np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
# Slaney coefficients will always produce 40 samples spanning 133Hz to 6000Hz
if self._config['coeffs_type'] == 'slaney':
self.filterbank = aubio.filterbank(40, self._config['fft_size'])
self.filterbank.set_mel_coeffs_slaney(self._config['mic_rate'])
# Sanley frequencies are linear-log spaced where 133Hz to 1000Hz is linear
# spaced and 1000Hz to 6000Hz is log spaced. It also produced a hardcoded
# 40 samples.
lowestFrequency = 133.3
linearSpacing = 66.6666666
logSpacing = 1.0711703
linearFilters = 13
logFilters = 27
linearSpacedFreqs = lowestFrequency + \
np.arange(0, linearFilters) * linearSpacing
logSpacedFreqs = linearSpacedFreqs[-1] * \
np.power(logSpacing, np.arange(1, logFilters + 1))
self._config['samples'] = 40
self.melbank_frequencies = np.hstack(
(linearSpacedFreqs, logSpacedFreqs)).astype(np.float32)
# Standard mel coefficients
if self._config['coeffs_type'] == 'mel':
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_mel_coeffs(self._config['mic_rate'],
self._config['min_frequency'],
self._config['max_frequency'])
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'])
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel])
# HTK mel coefficients
if self._config['coeffs_type'] == 'htk':
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_mel_coeffs_htk(self._config['mic_rate'],
self._config['min_frequency'],
self._config['max_frequency'])
# Frequencies wil be linearly spaced in the mel scale
melbank_mel = np.linspace(
aubio.hztomel(self._config['min_frequency']),
aubio.hztomel(self._config['max_frequency']),
self._config['samples'])
self.melbank_frequencies = np.array(
[aubio.meltohz(mel) for mel in melbank_mel])
# Coefficients based on Scott's audio reactive led project
if self._config['coeffs_type'] == 'scott':
(melmat, center_frequencies_hz, freqs) = mel.compute_melmat(
num_mel_bands=self._config['samples'],
freq_min=self._config['min_frequency'],
freq_max=self._config['max_frequency'],
num_fft_bands=int(self._config['fft_size'] // 2) + 1,
sample_rate=self._config['mic_rate'])
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
# "Mel"-spacing based on Scott's audio reactive led project. This
# should in theory be the same as the above, but there seems to be
# slight differences. Leaving both for science!
if self._config['coeffs_type'] == 'scott_mel':
def hertz_to_scott(freq):
return 3340.0 * log(1 + (freq / 250.0), 9)
def scott_to_hertz(scott):
return 250.0 * (9**(scott / 3340.0)) - 250.0
melbank_scott = np.linspace(
hertz_to_scott(self._config['min_frequency']),
hertz_to_scott(self._config['max_frequency']),
self._config['samples'] + 2)
self.melbank_frequencies = np.array([
scott_to_hertz(scott) for scott in melbank_scott
]).astype(np.float32)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_triangle_bands(self.melbank_frequencies,
self._config['mic_rate'])
self.melbank_frequencies = self.melbank_frequencies[1:-1]
if self._config['coeffs_type'] == 'fixed':
ranges = FREQUENCY_RANGES.values()
upper_edges_hz = np.zeros(len(ranges))
lower_edges_hz = np.zeros(len(ranges))
for idx, value in enumerate(ranges):
lower_edges_hz[idx] = value.min
upper_edges_hz[idx] = value.max
(melmat, center_frequencies_hz,
freqs) = mel.compute_melmat_from_range(
lower_edges_hz=lower_edges_hz,
upper_edges_hz=upper_edges_hz,
num_fft_bands=int(self._config['fft_size'] // 2) + 1,
sample_rate=self._config['mic_rate'])
self._config['samples'] = len(center_frequencies_hz)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
if self._config['coeffs_type'] == 'fixed_simple':
ranges = FREQUENCY_RANGES_SIMPLE.values()
upper_edges_hz = np.zeros(len(ranges))
lower_edges_hz = np.zeros(len(ranges))
for idx, value in enumerate(ranges):
lower_edges_hz[idx] = value.min
upper_edges_hz[idx] = value.max
(melmat, center_frequencies_hz,
freqs) = mel.compute_melmat_from_range(
lower_edges_hz=lower_edges_hz,
upper_edges_hz=upper_edges_hz,
num_fft_bands=int(self._config['fft_size'] // 2) + 1,
sample_rate=self._config['mic_rate'])
self._config['samples'] = len(center_frequencies_hz)
self.filterbank = aubio.filterbank(self._config['samples'],
self._config['fft_size'])
self.filterbank.set_coeffs(melmat.astype(np.float32))
self.melbank_frequencies = center_frequencies_hz
self.melbank_frequencies = self.melbank_frequencies.astype(int)
# Normalize the filterbank triangles to a consistent height, the
# default coeffs (for types other than legacy) will be normalized
# by the triangles area which results in an uneven melbank
if self._config['coeffs_type'] != 'scott' and self._config[
'coeffs_type'] == 'scott_mel':
coeffs = self.filterbank.get_coeffs()
coeffs /= np.max(coeffs, axis=-1)[:, None]
self.filterbank.set_coeffs(coeffs)
# Find the indexes for each of the frequency ranges
self.lows_index = self.mids_index = self.highs_index = 1
for i in range(0, len(self.melbank_frequencies)):
if self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE['low'].max:
self.lows_index = i + 1
elif self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE[
'mid'].max:
self.mids_index = i + 1
elif self.melbank_frequencies[i] < FREQUENCY_RANGES_SIMPLE[
'high'].max:
self.highs_index = i + 1
# Build up some of the common filters
self.mel_gain = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.01,
alpha_rise=0.99)
self.mel_smoothing = ExpFilter(np.tile(1e-1, self._config['samples']),
alpha_decay=0.2,
alpha_rise=0.99)
self.common_filter = ExpFilter(alpha_decay=0.99, alpha_rise=0.01)
@lru_cache(maxsize=32)
def _gaussian_kernel1d(self, sigma, order, radius):
if order < 0:
raise ValueError('order must be non-negative')
p = np.polynomial.Polynomial([0, 0, -0.5 / (sigma * sigma)])
x = np.arange(-radius, radius + 1)
phi_x = np.exp(p(x), dtype=np.double)
phi_x /= phi_x.sum()
if order > 0:
q = np.polynomial.Polynomial([1])
p_deriv = p.deriv()
for _ in range(order):
# f(x) = q(x) * phi(x) = q(x) * exp(p(x))
# f'(x) = (q'(x) + q(x) * p'(x)) * phi(x)
q = q.deriv() + q * p_deriv
phi_x *= q(x)
return phi_x
def smooth(self, x, sigma):
lw = int(4.0 * float(sigma) + 0.5)
w = self._gaussian_kernel1d(sigma, 0, lw)
window_len = len(w)
s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]
y = np.convolve(w / w.sum(), s, mode='valid')
if window_len < len(x):
return y[(window_len // 2):-(window_len // 2)]
return y[0:len(x)]
@lru_cache(maxsize=32)
def melbank(self):
"""Returns the raw melbank curve"""
if self.volume() > self._config['min_volume']:
# Compute the filterbank from the frequency information
raw_filter_banks = self.filterbank(self.frequency_domain())
raw_filter_banks = raw_filter_banks**2.0
self.mel_gain.update(
np.max(self.smooth(raw_filter_banks, sigma=1.0)))
filter_banks = raw_filter_banks / self.mel_gain.value
filter_banks = self.mel_smoothing.update(filter_banks)
# print(self.onset_mids(self.audio_sample())[0])
# specdesc_high = self.specdesc_o_high(self._frequency_domain)[0]
# print(specdesc_high)
else:
raw_filter_banks = np.zeros(self._config['samples'])
filter_banks = raw_filter_banks
if self._ledfx.dev_enabled():
self._ledfx.events.fire_event(
GraphUpdateEvent('raw', raw_filter_banks,
np.array(self.melbank_frequencies)))
self._ledfx.events.fire_event(
GraphUpdateEvent('melbank', filter_banks,
np.array(self.melbank_frequencies)))
return filter_banks
def melbank_lows(self):
return self.melbank()[:self.lows_index]
def melbank_mids(self):
return self.melbank()[self.lows_index:self.mids_index]
def melbank_highs(self):
return self.melbank()[self.mids_index:]
@lru_cache(maxsize=32)
def melbank_filtered(self):
# TODO: Should probably account for the filtered melbank not being
# queried every frame which would result in a poor filter. Need a
# good balance between wasting compute resources and quality filters.
return self.common_filter.update(self.melbank())
def sample_melbank(self, hz):
"""Samples the melbank curve at a given frequency"""
return np.interp(hz, self.melbank_frequencies, self.melbank())
@lru_cache(maxsize=32)
def interpolated_melbank(self, size, filtered=True):
"""Returns a melbank curve interpolated up to a given size"""
if filtered is True:
return math.interpolate(self.melbank_filtered(), size)
return math.interpolate(self.melbank(), size)
@lru_cache(maxsize=32)
def midi_value(self):
return self.pitch_o(self.audio_sample())[0]
def onset(self):
return self.onset_o(self.audio_sample())[0]
def oscillator(self):
"""
returns a float (0<=x<1) corresponding to the current position of beat tracker.
this is synced and quantized to the bpm of whatever is playing.
0 0.5 <1
{----------time for one beat---------}
^ --> --> -->
value of
oscillator
"""
# update tempo and oscillator
is_beat = bool(self.tempo_o(self.audio_sample(raw=True))[0])
if is_beat:
self.beat_period = self.tempo_o.get_period_s()
self.beat_timestamp = time.time()
oscillator = 0
else:
time_since_beat = time.time() - self.beat_timestamp
oscillator = 1 - (self.beat_period -
time_since_beat) / self.beat_period
# ensure it's between 0 and 1. useful when audio cuts
oscillator = min(1, oscillator)
oscillator = max(0, oscillator)
return oscillator, is_beat
