Help on module S3DataUtils:
NAME
S3DataUtils - Utils Functions involving usage of DataFrame
FUNCTIONS
create_FunctionFrame(fs: int, Ns: int, Ss: int) -> pandas.core.frame.DataFrame
Takes Sampling Frequency and returns a DataFrame
with function vectors of frequencies
predict_fs(fs: int, Ns: int, Ss: int, reg: sklearn.linear_model.base.LinearRegression) -> numpy.ndarray
Returns predicted signal of given frequency
Ss is sample rate
Fs is natural frequency
Ns is number of samples
train_S3(FuncFrame: pandas.core.frame.DataFrame, sig: numpy.ndarray) -> sklearn.linear_model.base.LinearRegression
Function That trains FuncFrame on input signal
Returns:
LinearRegression
FILE
/mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3DataUtils.py
Help on module S3GuiMain:
NAME
S3GuiMain - # -*- coding: utf-8 -*-
CLASSES
builtins.object
Ui_MainWindow
class Ui_MainWindow(builtins.object)
| Methods defined here:
|
| retranslateUi(self, MainWindow)
|
| setupUi(self, MainWindow)
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
FILE
/mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3GuiMain.py
Help on module S3Synth:
NAME
S3Synth - Synthesiser Class of S3
CLASSES
builtins.object
S3Synth
class S3Synth(builtins.object)
| S3Synth(wavecoef_: numpy.ndarray, transpo=1, mul=1)
|
| Main Synth Class that manages backend of Synthesiser
|
| Methods defined here:
|
| __init__(self, wavecoef_: numpy.ndarray, transpo=1, mul=1)
| Initialize self. See help(type(self)) for accurate signature.
|
| out(self)
| Sends the synth's signal to the audio output and return the object itself.
|
| sig(self)
| Returns the synth's signal for future processing.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
FUNCTIONS
random(...) method of random.Random instance
random() -> x in the interval [0, 1).
FILE
/mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3Synth.py
Help on module S3SignalUtils:
NAME
S3SignalUtils - Utils function related to signals for S3
FUNCTIONS
cos(fs: float, Ns: int, Ss: int) -> numpy.ndarray
Returns a Cosine wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs
filt_bp(sig: numpy.ndarray, Ss: int, Cfs0: int, Cfs1: None, order=5) -> numpy.ndarray
return a filtered signal; band pass filter
filt_hp(sig: numpy.ndarray, Ss: int, Cfs: int, Cfs1: None, order=5) -> numpy.ndarray
return a filtered signal; high pass filter
filt_lp(sig: numpy.ndarray, Ss: int, Cfs: int, Cfs1: None, order=5) -> numpy.ndarray
return a filtered signal; low pass filter
sawtooth(fs: float, Ns: int, Ss: int) -> numpy.ndarray
Returns a Sawtooth wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs
sigin(wavname: str) -> Tuple[int, numpy.ndarray]
Functions that reads wave file and return sample rate and signal as np.array
sin(fs: float, Ns: int, Ss: int) -> numpy.ndarray
Returns a Sine wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs
triangle(fs: float, Ns: int, Ss: int) -> numpy.ndarray
Returns a Triangle wave of Sample rate Ss with Ns number of samples and Sample Frequency Fs
DATA
Tuple = typing.Tuple
FILE
/mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3SignalUtils.py
Help on module S3Utils:
NAME
S3Utils - Utils Functions for S3 Synthesiser App
FUNCTIONS
create_env(sig: numpy.ndarray, Fs: float, Ss: int, Ns: int) -> numpy.ndarray
return envelope of signal
create_partial_envelope(sig: numpy.ndarray, Fs: float, Ss: int) -> numpy.ndarray
Creates a partial envelope using min and max of in one cycle.
find_Ns(Freq: float, Ss: int) -> int
Finds the Ns for Training Phase
find_maxsig(sig: numpy.ndarray, Ns: int) -> numpy.ndarray
returns part of signal where its in constant sustain
freq_calc(sig: numpy.ndarray, Ss: int) -> float
Calculates the average frequency of the input signal (of a recorded note)
freq_from_HPS(sig, fs)
Estimate frequency using harmonic product spectrum (HPS)
freq_from_autocorr(sig, fs)
Estimate frequency using autocorrelation
freq_from_crossings(sig, fs)
Estimate frequency by counting zero crossings
freq_from_fft(sig, fs)
Estimate frequency from peak of FFT
get_note(freq: float) -> Tuple[float, str]
Returns the Note (and its Natural Frequency)
corresponding to input frequency
make_natural_env(env: numpy.ndarray, Ns: int) -> numpy.ndarray
Returns an envelope in natural time for the
signal by upsampling and uniforming partial envelope
make_octaves() -> numpy.ndarray
Creates Octaves with their corresponding frequency
time(...)
time() -> floating point number
Return the current time in seconds since the Epoch.
Fractions of a second may be present if the system clock provides them.
DATA
Tuple = typing.Tuple
log =
FILE
/mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3Utils.py
Help on module S3SynthMain:
NAME
S3SynthMain
CLASSES
builtins.object
S3App
class S3App(builtins.object)
| Class to manage interface of S3 Synthesiser
|
| Methods defined here:
|
| __init__(self)
| Initialize self. See help(type(self)) for accurate signature.
|
| load_file(self, file_path: str)
| Loads a Sample into the synthesiser
|
| load_trainedsynth(self)
| Loads all properties of S3 trains S3 and initialises S3Synth
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
FUNCTIONS
main()
Driver code
FILE
/mnt/4427FDEE206BF5AE/Documents/codes/S3_Smart_Sampling_Synthesiser/S3SynthMain.py