Pitch

class audioflux.PitchCEP(samplate=32000, low_fre=32.0, high_fre=2000.0, radix2_exp=12, slide_length=1024, window_type=WindowType.HAMM)

Pitch CEP algorithm

Parameters

samplate: int

Sampling rate of the incoming audio.

low_fre: float

Lowest frequency. Default is 32.0.

high_fre: float

Highest frequency. Default is 2000.0.

radix2_exp: int

fft_length=2**radix2_exp

slide_length: int

Window sliding length.

window_type: WindowType

Window type for each frame.

See: type.WindowType

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Extract pitch

>>> pitch_obj = af.PitchCEP(samplate=sr)
>>> fre_arr = pitch_obj.pitch(audio_arr)

Show pitch plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_plot
>>> times = np.arange(fre_arr.shape[-1]) * (pitch_obj.slide_length / sr)
>>> fig, ax = plt.subplots()
>>> ax.set_title('PitchCEP')
>>> fill_plot(times, fre_arr, axes=ax)
>>> ax.set_ylim(0, 300)

Methods

`cal_time_length`(data_length)	Calculate the length of a frame from audio data.
`pitch`(data_arr)	Compute pitch

cal_time_length(data_length)

Calculate the length of a frame from audio data.

fft_length = 2 ** radix2_exp
(data_length - fft_length) // slide_length + 1

Parameters

data_length: int: The length of the data to be calculated.

Returns

out: int

pitch(data_arr)

Compute pitch

Parameters

data_arr: np.ndarray [shape=(…, n)]: Input audio array

Returns

fre_arr: np.ndarray [shape=(…, time)]

class audioflux.PitchHPS(samplate=32000, low_fre=32.0, high_fre=2000.0, radix2_exp=12, slide_length=1024, window_type=WindowType.HAMM, harmonic_count=5)

Pitch HPS algorithm

Parameters

samplate: int

Sampling rate of the incoming audio.

low_fre: float

Lowest frequency. Default is 32.0.

high_fre: float

Highest frequency. Default is 2000.0.

radix2_exp: int

fft_length=2**radix2_exp

slide_length: int

Window sliding length.

window_type: WindowType

Window type for each frame.

See: type.WindowType

harmonic_count: int

Harmonic count. Default is 5.

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Extract pitch

>>> pitch_obj = af.PitchHPS(samplate=sr)
>>> fre_arr = pitch_obj.pitch(audio_arr)

Show pitch plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_plot
>>> times = np.arange(fre_arr.shape[-1]) * (pitch_obj.slide_length / sr)
>>> fig, ax = plt.subplots()
>>> ax.set_title('PitchHPS')
>>> fill_plot(times, fre_arr, axes=ax)
>>> ax.set_ylim(0, 300)

Methods

`cal_time_length`(data_length)	Calculate the length of a frame from audio data.
`pitch`(data_arr)	Compute pitch

cal_time_length(data_length)

Calculate the length of a frame from audio data.

fft_length = 2 ** radix2_exp
(data_length - fft_length) // slide_length + 1

Parameters

data_length: int: The length of the data to be calculated.

Returns

out: int

pitch(data_arr)

Compute pitch

Parameters

data_arr: np.ndarray [shape=(…, n)]: Input audio array

Returns

fre_arr: np.ndarray [shape=(…, time)]

class audioflux.PitchLHS(samplate=32000, low_fre=32.0, high_fre=2000.0, radix2_exp=12, slide_length=1024, window_type=WindowType.HAMM, harmonic_count=5)

Pitch LHS algorithm

Parameters

samplate: int

Sampling rate of the incoming audio.

low_fre: float

Lowest frequency. Default is 32.0.

high_fre: float

Highest frequency. Default is 2000.0.

radix2_exp: int

fft_length=2**radix2_exp

slide_length: int

Window sliding length.

window_type: WindowType

Window type for each frame.

See: type.WindowType

harmonic_count: int

Harmonic count. Default is 5.

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Extract pitch

>>> pitch_obj = af.PitchLHS(samplate=sr)
>>> fre_arr = pitch_obj.pitch(audio_arr)

Show pitch plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_plot
>>> times = np.arange(fre_arr.shape[-1]) * (pitch_obj.slide_length / sr)
>>> fig, ax = plt.subplots()
>>> ax.set_title('PitchLHS')
>>> fill_plot(times, fre_arr, axes=ax)
>>> ax.set_ylim(0, 300)

Methods

`cal_time_length`(data_length)	Calculate the length of a frame from audio data.
`pitch`(data_arr)	Compute pitch

cal_time_length(data_length)

Calculate the length of a frame from audio data.

fft_length = 2 ** radix2_exp
(data_length - fft_length) / slide_length + 1

Parameters

data_length: int: The length of the data to be calculated.

Returns

out: int

pitch(data_arr)

Compute pitch

Parameters

data_arr: np.ndarray [shape=(…, n)]: Input audio array

Returns

fre_arr: np.ndarray [shape=(…, time)]

class audioflux.PitchNCF(samplate=32000, low_fre=32.0, high_fre=2000.0, radix2_exp=12, slide_length=1024, window_type=WindowType.RECT)

Pitch NCF algorithm

Parameters

samplate: int

Sampling rate of the incoming audio.

low_fre: float

Lowest frequency. Default is 32.0.

high_fre: float

Highest frequency. Default is 2000.0.

radix2_exp: int

fft_length=2**radix2_exp

slide_length: int

Window sliding length.

window_type: WindowType

Window type for each frame.

See: type.WindowType

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Extract pitch

>>> pitch_obj = af.PitchNCF(samplate=sr)
>>> fre_arr = pitch_obj.pitch(audio_arr)

Show pitch plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_plot
>>> times = np.arange(fre_arr.shape[-1]) * (pitch_obj.slide_length / sr)
>>> fig, ax = plt.subplots()
>>> ax.set_title('PitchNCF')
>>> fill_plot(times, fre_arr, axes=ax)
>>> ax.set_ylim(0, 300)

Methods

`cal_time_length`(data_length)	Calculate the length of a frame from audio data.
`pitch`(data_arr)	Compute pitch

cal_time_length(data_length)

Calculate the length of a frame from audio data.

fft_length = 2 ** radix2_exp
(data_length - fft_length) / slide_length + 1

Parameters

data_length: int: The length of the data to be calculated.

Returns

out: int

pitch(data_arr)

Compute pitch

Parameters

data_arr: np.ndarray [shape=(…, n)]: Input audio array

Returns

fre_arr: np.ndarray [shape=(…, time)]

class audioflux.PitchPEF(samplate=32000, low_fre=32.0, high_fre=2000.0, cut_fre=4000.0, radix2_exp=12, slide_length=1024, window_type=WindowType.HAMM, alpha=10.0, beta=0.5, gamma=1.8)

Pitch PEF algorithm

Parameters

samplate: int

Sampling rate of the incoming audio.

low_fre: float

Lowest frequency. Default is 32.0.

high_fre: float

Highest frequency. Default is 2000.0.

cut_fre: float

Cut frequency. Default is 4000.0, and must be greater than high_fre.

radix2_exp: int

fft_length=2**radix2_exp

slide_length: int

Window sliding length.

window_type: WindowType

Window type for each frame.

See: type.WindowType

alpha: float, > 0

alpha. Default if 10.0..

beta: float, 0~1

beta. Default if 0.5..

gamma: float, > 1

gamma. Default if 1.8.

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Extract pitch

>>> pitch_obj = af.PitchPEF(samplate=sr)
>>> fre_arr = pitch_obj.pitch(audio_arr)

Show pitch plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_plot
>>> times = np.arange(fre_arr.shape[-1]) * (pitch_obj.slide_length / sr)
>>> fig, ax = plt.subplots()
>>> ax.set_title('PitchPEF')
>>> fill_plot(times, fre_arr, axes=ax)
>>> ax.set_ylim(0, 300)

Methods

`cal_time_length`(data_length)	Calculate the length of a frame from audio data.
`pitch`(data_arr)	Compute pitch
`set_filter_params`(alpha, beta, gamma)	Set filter params

cal_time_length(data_length)

Calculate the length of a frame from audio data.

fft_length = 2 ** radix2_exp
(data_length - fft_length) // slide_length + 1

Parameters

data_length: int: The length of the data to be calculated.

Returns

out: int

set_filter_params(alpha, beta, gamma)

Set filter params

Parameters

alpha: float: alpha
beta: float: beta
gamma: float: gamma

pitch(data_arr)

Compute pitch

Parameters

data_arr: np.ndarray [shape=(…, n)]: Input audio array

Returns

fre_arr: np.ndarray [shape=(…, time)]

class audioflux.PitchSTFT(samplate=32000, low_fre=32.0, high_fre=2000.0, radix2_exp=12, slide_length=1024, window_type=WindowType.HAMM)

Pitch STFT algorithm

Parameters

samplate: int

Sampling rate of the incoming audio.

low_fre: float

Lowest frequency. Default is 32.0.

high_fre: float

Highest frequency. Default is 2000.0.

radix2_exp: int

fft_length=2**radix2_exp

slide_length: int

Window sliding length.

window_type: WindowType

Window type for each frame.

See: type.WindowType

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Extract pitch

>>> pitch_obj = af.PitchSTFT(samplate=sr)
>>> fre_arr, db_arr = pitch_obj.pitch(audio_arr)

Show pitch plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_plot
>>> times = np.arange(fre_arr.shape[-1]) * (pitch_obj.slide_length / sr)
>>> fig, ax = plt.subplots()
>>> ax.set_title('PitchSTFT')
>>> fill_plot(times, fre_arr, axes=ax)
>>> ax.set_ylim(0, 300)

Methods

`cal_time_length`(data_length)	Calculate the length of a frame from audio data.
`pitch`(data_arr)	Compute pitch

cal_time_length(data_length)

Calculate the length of a frame from audio data.

fft_length = 2 ** radix2_exp
(data_length - fft_length) // slide_length + 1

Parameters

data_length: int: The length of the data to be calculated.

Returns

out: int

pitch(data_arr)

Compute pitch

Parameters

data_arr: np.ndarray [shape=(…, n)]: Input audio array

Returns

fre_arr: np.ndarray [shape=(…, time)]
db_arr: np.ndarray [shape=(…, time)]

class audioflux.PitchYIN(samplate=32000, low_fre=27.0, high_fre=2000.0, radix2_exp=12, slide_length=1024, auto_length=2048)

Pitch YIN algorithm

Parameters

samplate: int: Sampling rate of the incoming audio.
low_fre: float: Lowest frequency. Default is 27.0.
high_fre: float: Highest frequency. Default is 2000.0.
radix2_exp: int: fft_length=2**radix2_exp
slide_length: int: Window sliding length.
auto_length: int: Auto correlation length. Default is 2048.

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Extract pitch

>>> pitch_obj = af.PitchYIN(samplate=sr)
>>> fre_arr, v1_arr, v2_arr = pitch_obj.pitch(audio_arr)

Show pitch plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_plot
>>> times = np.arange(fre_arr.shape[-1]) * (pitch_obj.slide_length / sr)
>>> fig, ax = plt.subplots()
>>> ax.set_title('PitchYIN')
>>> fill_plot(times, fre_arr, axes=ax)
>>> ax.set_ylim(0, 300)

Methods

`cal_time_length`(data_length)	Calculate the length of a frame from audio data.
`pitch`(data_arr)	Compute pitch
`set_thresh`(thresh)	Set thresh

set_thresh(thresh)

Set thresh

Parameters

thresh: float

cal_time_length(data_length)

Calculate the length of a frame from audio data.

fft_length = 2 ** radix2_exp
(data_length - fft_length) // slide_length + 1

Parameters

data_length: int: The length of the data to be calculated.

Returns

out: int

pitch(data_arr)

Compute pitch

Parameters

data_arr: np.ndarray [shape=(…, n)]: Input audio array

Returns

fre_arr: np.ndarray [shape=(…, time)]
value1_arr: np.ndarray [shape=(…, time)]
value2_arr: np.ndarray [shape=(…, time)]