[docs]defframes_to_samples(frames:_ScalarOrSequence[_IntLike_co],*,hop_length:int=512,n_fft:Optional[int]=None,)->Union[np.integer[Any],np.ndarray]:"""Convert frame indices to audio sample indices. Parameters ---------- frames : number or np.ndarray [shape=(n,)] frame index or vector of frame indices hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. Returns ------- times : number or np.ndarray time (in samples) of each given frame number:: times[i] = frames[i] * hop_length See Also -------- frames_to_time : convert frame indices to time values samples_to_frames : convert sample indices to frame indices Examples -------- >>> y, sr = librosa.load(librosa.ex('choice')) >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) >>> beat_samples = librosa.frames_to_samples(beats, sr=sr) """offset=0ifn_fftisnotNone:offset=int(n_fft//2)return(np.asanyarray(frames)*hop_length+offset).astype(int)
[docs]defsamples_to_frames(samples:_ScalarOrSequence[_IntLike_co],*,hop_length:int=512,n_fft:Optional[int]=None,)->Union[np.integer[Any],np.ndarray]:"""Convert sample indices into STFT frames. Examples -------- >>> # Get the frame numbers for every 256 samples >>> librosa.samples_to_frames(np.arange(0, 22050, 256)) array([ 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 35, 35, 36, 36, 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42, 42, 43]) Parameters ---------- samples : int or np.ndarray [shape=(n,)] sample index or vector of sample indices hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``- n_fft // 2`` to counteract windowing effects in STFT. .. note:: This may result in negative frame indices. Returns ------- frames : int or np.ndarray [shape=(n,), dtype=int] Frame numbers corresponding to the given times:: frames[i] = floor( samples[i] / hop_length ) See Also -------- samples_to_time : convert sample indices to time values frames_to_samples : convert frame indices to sample indices """offset=0ifn_fftisnotNone:offset=int(n_fft//2)samples=np.asanyarray(samples)returnnp.asarray(np.floor((samples-offset)//hop_length),dtype=int)
[docs]defframes_to_time(frames:_ScalarOrSequence[_IntLike_co],*,sr:float=22050,hop_length:int=512,n_fft:Optional[int]=None,)->Union[np.floating[Any],np.ndarray]:"""Convert frame counts to time (seconds). Parameters ---------- frames : np.ndarray [shape=(n,)] frame index or vector of frame indices sr : number > 0 [scalar] audio sampling rate hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. Returns ------- times : np.ndarray [shape=(n,)] time (in seconds) of each given frame number:: times[i] = frames[i] * hop_length / sr See Also -------- time_to_frames : convert time values to frame indices frames_to_samples : convert frame indices to sample indices Examples -------- >>> y, sr = librosa.load(librosa.ex('choice')) >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) >>> beat_times = librosa.frames_to_time(beats, sr=sr) """samples=frames_to_samples(frames,hop_length=hop_length,n_fft=n_fft)returnsamples_to_time(samples,sr=sr)
[docs]deftime_to_frames(times:_ScalarOrSequence[_FloatLike_co],*,sr:float=22050,hop_length:int=512,n_fft:Optional[int]=None,)->Union[np.integer[Any],np.ndarray]:"""Convert time stamps into STFT frames. Parameters ---------- times : np.ndarray [shape=(n,)] time (in seconds) or vector of time values sr : number > 0 [scalar] audio sampling rate hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``- n_fft // 2`` to counteract windowing effects in STFT. .. note:: This may result in negative frame indices. Returns ------- frames : np.ndarray [shape=(n,), dtype=int] Frame numbers corresponding to the given times:: frames[i] = floor( times[i] * sr / hop_length ) See Also -------- frames_to_time : convert frame indices to time values time_to_samples : convert time values to sample indices Examples -------- Get the frame numbers for every 100ms >>> librosa.time_to_frames(np.arange(0, 1, 0.1), ... sr=22050, hop_length=512) array([ 0, 4, 8, 12, 17, 21, 25, 30, 34, 38]) """samples=time_to_samples(times,sr=sr)returnsamples_to_frames(samples,hop_length=hop_length,n_fft=n_fft)
[docs]deftime_to_samples(times:_ScalarOrSequence[_FloatLike_co],*,sr:float=22050)->Union[np.integer[Any],np.ndarray]:"""Convert timestamps (in seconds) to sample indices. Parameters ---------- times : number or np.ndarray Time value or array of time values (in seconds) sr : number > 0 Sampling rate Returns ------- samples : int or np.ndarray [shape=times.shape, dtype=int] Sample indices corresponding to values in ``times`` See Also -------- time_to_frames : convert time values to frame indices samples_to_time : convert sample indices to time values Examples -------- >>> librosa.time_to_samples(np.arange(0, 1, 0.1), sr=22050) array([ 0, 2205, 4410, 6615, 8820, 11025, 13230, 15435, 17640, 19845]) """return(np.asanyarray(times)*sr).astype(int)
[docs]defsamples_to_time(samples:_ScalarOrSequence[_IntLike_co],*,sr:float=22050)->Union[np.floating[Any],np.ndarray]:"""Convert sample indices to time (in seconds). Parameters ---------- samples : np.ndarray Sample index or array of sample indices sr : number > 0 Sampling rate Returns ------- times : np.ndarray [shape=samples.shape] Time values corresponding to ``samples`` (in seconds) See Also -------- samples_to_frames : convert sample indices to frame indices time_to_samples : convert time values to sample indices Examples -------- Get timestamps corresponding to every 512 samples >>> librosa.samples_to_time(np.arange(0, 22050, 512), sr=22050) array([ 0. , 0.023, 0.046, 0.07 , 0.093, 0.116, 0.139, 0.163, 0.186, 0.209, 0.232, 0.255, 0.279, 0.302, 0.325, 0.348, 0.372, 0.395, 0.418, 0.441, 0.464, 0.488, 0.511, 0.534, 0.557, 0.58 , 0.604, 0.627, 0.65 , 0.673, 0.697, 0.72 , 0.743, 0.766, 0.789, 0.813, 0.836, 0.859, 0.882, 0.906, 0.929, 0.952, 0.975, 0.998]) """returnnp.asanyarray(samples)/float(sr)
[docs]defblocks_to_frames(blocks:_ScalarOrSequence[_IntLike_co],*,block_length:int)->Union[np.integer[Any],np.ndarray]:"""Convert block indices to frame indices Parameters ---------- blocks : np.ndarray Block index or array of block indices block_length : int > 0 The number of frames per block Returns ------- frames : np.ndarray [shape=samples.shape, dtype=int] The index or indices of frames corresponding to the beginning of each provided block. See Also -------- blocks_to_samples blocks_to_time Examples -------- Get frame indices for each block in a stream >>> filename = librosa.ex('brahms') >>> sr = librosa.get_samplerate(filename) >>> stream = librosa.stream(filename, block_length=16, ... frame_length=2048, hop_length=512) >>> for n, y in enumerate(stream): ... n_frame = librosa.blocks_to_frames(n, block_length=16) """returnblock_length*np.asanyarray(blocks)
[docs]defblocks_to_samples(blocks:_ScalarOrSequence[_IntLike_co],*,block_length:int,hop_length:int)->Union[np.integer[Any],np.ndarray]:"""Convert block indices to sample indices Parameters ---------- blocks : np.ndarray Block index or array of block indices block_length : int > 0 The number of frames per block hop_length : int > 0 The number of samples to advance between frames Returns ------- samples : np.ndarray [shape=samples.shape, dtype=int] The index or indices of samples corresponding to the beginning of each provided block. Note that these correspond to the *first* sample index in each block, and are not frame-centered. See Also -------- blocks_to_frames blocks_to_time Examples -------- Get sample indices for each block in a stream >>> filename = librosa.ex('brahms') >>> sr = librosa.get_samplerate(filename) >>> stream = librosa.stream(filename, block_length=16, ... frame_length=2048, hop_length=512) >>> for n, y in enumerate(stream): ... n_sample = librosa.blocks_to_samples(n, block_length=16, ... hop_length=512) """frames=blocks_to_frames(blocks,block_length=block_length)returnframes_to_samples(frames,hop_length=hop_length)
[docs]defblocks_to_time(blocks:_ScalarOrSequence[_IntLike_co],*,block_length:int,hop_length:int,sr:float,)->Union[np.floating[Any],np.ndarray]:"""Convert block indices to time (in seconds) Parameters ---------- blocks : np.ndarray Block index or array of block indices block_length : int > 0 The number of frames per block hop_length : int > 0 The number of samples to advance between frames sr : int > 0 The sampling rate (samples per second) Returns ------- times : np.ndarray [shape=samples.shape] The time index or indices (in seconds) corresponding to the beginning of each provided block. Note that these correspond to the time of the *first* sample in each block, and are not frame-centered. See Also -------- blocks_to_frames blocks_to_samples Examples -------- Get time indices for each block in a stream >>> filename = librosa.ex('brahms') >>> sr = librosa.get_samplerate(filename) >>> stream = librosa.stream(filename, block_length=16, ... frame_length=2048, hop_length=512) >>> for n, y in enumerate(stream): ... n_time = librosa.blocks_to_time(n, block_length=16, ... hop_length=512, sr=sr) """samples=blocks_to_samples(blocks,block_length=block_length,hop_length=hop_length)returnsamples_to_time(samples,sr=sr)
[docs]defnote_to_hz(note:Union[str,_IterableLike[str],Iterable[str]],**kwargs:Any)->Union[np.floating[Any],np.ndarray]:"""Convert one or more note names to frequency (Hz) Examples -------- >>> # Get the frequency of a note >>> librosa.note_to_hz('C') array([ 16.352]) >>> # Or multiple notes >>> librosa.note_to_hz(['A3', 'A4', 'A5']) array([ 220., 440., 880.]) >>> # Or notes with tuning deviations >>> librosa.note_to_hz('C2-32', round_midi=False) array([ 64.209]) Parameters ---------- note : str or iterable of str One or more note names to convert **kwargs : additional keyword arguments Additional parameters to `note_to_midi` Returns ------- frequencies : number or np.ndarray [shape=(len(note),)] Array of frequencies (in Hz) corresponding to ``note`` See Also -------- midi_to_hz note_to_midi hz_to_note """returnmidi_to_hz(note_to_midi(note,**kwargs))
[docs]defnote_to_midi(note:Union[str,_IterableLike[str],Iterable[str]],*,round_midi:bool=True)->Union[float,np.ndarray]:"""Convert one or more spelled notes to MIDI number(s). Notes may be spelled out with optional accidentals or octave numbers. The leading note name is case-insensitive. Sharps are indicated with ``#``, flats may be indicated with ``!`` or ``b``. Parameters ---------- note : str or iterable of str One or more note names. round_midi : bool - If ``True``, midi numbers are rounded to the nearest integer. - If ``False``, allow fractional midi numbers. Returns ------- midi : float or np.array Midi note numbers corresponding to inputs. Raises ------ ParameterError If the input is not in valid note format See Also -------- midi_to_note note_to_hz Examples -------- >>> librosa.note_to_midi('C') 12 >>> librosa.note_to_midi('C#3') 49 >>> librosa.note_to_midi('C♯3') # Using Unicode sharp 49 >>> librosa.note_to_midi('C♭3') # Using Unicode flat 47 >>> librosa.note_to_midi('f4') 65 >>> librosa.note_to_midi('Bb-1') 10 >>> librosa.note_to_midi('A!8') 116 >>> librosa.note_to_midi('G𝄪6') # Double-sharp 93 >>> librosa.note_to_midi('B𝄫6') # Double-flat 93 >>> librosa.note_to_midi('C♭𝄫5') # Triple-flats also work 69 >>> # Lists of notes also work >>> librosa.note_to_midi(['C', 'E', 'G']) array([12, 16, 19]) """ifnotisinstance(note,str):returnnp.array([note_to_midi(n,round_midi=round_midi)forninnote])pitch_map:Dict[str,int]={"C":0,"D":2,"E":4,"F":5,"G":7,"A":9,"B":11,}acc_map:Dict[str,int]={"#":1,"":0,"b":-1,"!":-1,"♯":1,"𝄪":2,"♭":-1,"𝄫":-2,"♮":0,}match=notation.NOTE_RE.match(note)ifnotmatch:raiseParameterError(f"Improper note format: {note:s}")pitch=match.group("note").upper()offset=np.sum([acc_map[o]foroinmatch.group("accidental")])octave=match.group("octave")cents=match.group("cents")ifnotoctave:octave=0else:octave=int(octave)ifnotcents:cents=0else:cents=int(cents)*1e-2note_value:float=12*(octave+1)+pitch_map[pitch]+offset+centsifround_midi:returnint(np.round(note_value))else:returnnote_value
[docs]@vectorize(excluded=["octave","cents","key","unicode"])defmidi_to_note(midi:_ScalarOrSequence[_FloatLike_co],*,octave:bool=True,cents:bool=False,key:str="C:maj",unicode:bool=True,)->Union[str,np.ndarray]:"""Convert one or more MIDI numbers to note strings. MIDI numbers will be rounded to the nearest integer. Notes will be of the format 'C0', 'C♯0', 'D0', ... Examples -------- >>> librosa.midi_to_note(0) 'C-1' >>> librosa.midi_to_note(37) 'C♯2' >>> librosa.midi_to_note(37, unicode=False) 'C#2' >>> librosa.midi_to_note(-2) 'A♯-2' >>> librosa.midi_to_note(104.7) 'A7' >>> librosa.midi_to_note(104.7, cents=True) 'A7-30' >>> librosa.midi_to_note(np.arange(12, 24))) array(['C0', 'C♯0', 'D0', 'D♯0', 'E0', 'F0', 'F♯0', 'G0', 'G♯0', 'A0', 'A♯0', 'B0'], dtype='<U3') Use a key signature to resolve enharmonic equivalences >>> librosa.midi_to_note(range(12, 24), key='F:min') array(['C0', 'D♭0', 'D0', 'E♭0', 'E0', 'F0', 'G♭0', 'G0', 'A♭0', 'A0', 'B♭0', 'B0'], dtype='<U3') Parameters ---------- midi : int or iterable of int Midi numbers to convert. octave : bool If True, include the octave number cents : bool If true, cent markers will be appended for fractional notes. Eg, ``midi_to_note(69.3, cents=True) == 'A4+03'`` key : str A key signature to use when resolving enharmonic equivalences. unicode : bool If ``True`` (default), accidentals will use Unicode notation: ♭ or ♯ If ``False``, accidentals will use ASCII-compatible notation: b or # Returns ------- notes : str or np.ndarray of str Strings describing each midi note. Raises ------ ParameterError if ``cents`` is True and ``octave`` is False See Also -------- midi_to_hz note_to_midi hz_to_note key_to_notes """ifcentsandnotoctave:raiseParameterError("Cannot encode cents without octave information.")note_map=notation.key_to_notes(key=key,unicode=unicode)# mypy does not understand vectorization, suppress type checksnote_num=int(np.round(midi))# type: ignorenote_cents=int(100*np.around(midi-note_num,2))# type: ignorenote=note_map[note_num%12]ifoctave:note="{:s}{:0d}".format(note,int(note_num/12)-1)ifcents:note=f"{note:s}{note_cents:+02d}"returnnote
[docs]defmidi_to_hz(notes:_ScalarOrSequence[_FloatLike_co],)->Union[np.ndarray,np.floating[Any]]:"""Get the frequency (Hz) of MIDI note(s) Examples -------- >>> librosa.midi_to_hz(36) 65.406 >>> librosa.midi_to_hz(np.arange(36, 48)) array([ 65.406, 69.296, 73.416, 77.782, 82.407, 87.307, 92.499, 97.999, 103.826, 110. , 116.541, 123.471]) Parameters ---------- notes : int or np.ndarray [shape=(n,), dtype=int] midi number(s) of the note(s) Returns ------- frequency : number or np.ndarray [shape=(n,), dtype=float] frequency (frequencies) of ``notes`` in Hz See Also -------- hz_to_midi note_to_hz """return440.0*(2.0**((np.asanyarray(notes)-69.0)/12.0))
[docs]defhz_to_note(frequencies:_ScalarOrSequence[_FloatLike_co],**kwargs:Any)->Union[str,np.ndarray]:"""Convert one or more frequencies (in Hz) to the nearest note names. Parameters ---------- frequencies : float or iterable of float Input frequencies, specified in Hz **kwargs : additional keyword arguments Arguments passed through to `midi_to_note` Returns ------- notes : str or np.ndarray of str ``notes[i]`` is the closest note name to ``frequency[i]`` (or ``frequency`` if the input is scalar) See Also -------- hz_to_midi midi_to_note note_to_hz Examples -------- Get a single note name for a frequency >>> librosa.hz_to_note(440.0) 'A5' Get multiple notes with cent deviation >>> librosa.hz_to_note([32, 64], cents=True) ['C1-38', 'C2-38'] Get multiple notes, but suppress octave labels >>> librosa.hz_to_note(440.0 * (2.0 ** np.linspace(0, 1, 12)), ... octave=False) ['A', 'A#', 'B', 'C', 'C#', 'D', 'E', 'F', 'F#', 'G', 'G#', 'A'] """returnmidi_to_note(hz_to_midi(frequencies),**kwargs)
[docs]defhz_to_mel(frequencies:_ScalarOrSequence[_FloatLike_co],*,htk:bool=False)->Union[np.floating[Any],np.ndarray]:"""Convert Hz to Mels Examples -------- >>> librosa.hz_to_mel(60) 0.9 >>> librosa.hz_to_mel([110, 220, 440]) array([ 1.65, 3.3 , 6.6 ]) Parameters ---------- frequencies : number or np.ndarray [shape=(n,)] , float scalar or array of frequencies htk : bool use HTK formula instead of Slaney Returns ------- mels : number or np.ndarray [shape=(n,)] input frequencies in Mels See Also -------- mel_to_hz """frequencies=np.asanyarray(frequencies)ifhtk:mels:np.ndarray=2595.0*np.log10(1.0+frequencies/700.0)returnmels# Fill in the linear partf_min=0.0f_sp=200.0/3mels=(frequencies-f_min)/f_sp# Fill in the log-scale partmin_log_hz=1000.0# beginning of log region (Hz)min_log_mel=(min_log_hz-f_min)/f_sp# same (Mels)logstep=np.log(6.4)/27.0# step size for log regioniffrequencies.ndim:# If we have array data, vectorizelog_t=frequencies>=min_log_hzmels[log_t]=min_log_mel+np.log(frequencies[log_t]/min_log_hz)/logstepeliffrequencies>=min_log_hz:# If we have scalar data, heck directlymels=min_log_mel+np.log(frequencies/min_log_hz)/logstepreturnmels
[docs]defmel_to_hz(mels:_ScalarOrSequence[_FloatLike_co],*,htk:bool=False)->Union[np.floating[Any],np.ndarray]:"""Convert mel bin numbers to frequencies Examples -------- >>> librosa.mel_to_hz(3) 200. >>> librosa.mel_to_hz([1,2,3,4,5]) array([ 66.667, 133.333, 200. , 266.667, 333.333]) Parameters ---------- mels : np.ndarray [shape=(n,)], float mel bins to convert htk : bool use HTK formula instead of Slaney Returns ------- frequencies : np.ndarray [shape=(n,)] input mels in Hz See Also -------- hz_to_mel """mels=np.asanyarray(mels)ifhtk:return700.0*(10.0**(mels/2595.0)-1.0)# Fill in the linear scalef_min=0.0f_sp=200.0/3freqs=f_min+f_sp*mels# And now the nonlinear scalemin_log_hz=1000.0# beginning of log region (Hz)min_log_mel=(min_log_hz-f_min)/f_sp# same (Mels)logstep=np.log(6.4)/27.0# step size for log regionifmels.ndim:# If we have vector data, vectorizelog_t=mels>=min_log_melfreqs[log_t]=min_log_hz*np.exp(logstep*(mels[log_t]-min_log_mel))elifmels>=min_log_mel:# If we have scalar data, check directlyfreqs=min_log_hz*np.exp(logstep*(mels-min_log_mel))returnfreqs
[docs]defhz_to_octs(frequencies:_ScalarOrSequence[_FloatLike_co],*,tuning:float=0.0,bins_per_octave:int=12,)->Union[np.floating[Any],np.ndarray]:"""Convert frequencies (Hz) to (fractional) octave numbers. Examples -------- >>> librosa.hz_to_octs(440.0) 4. >>> librosa.hz_to_octs([32, 64, 128, 256]) array([ 0.219, 1.219, 2.219, 3.219]) Parameters ---------- frequencies : number >0 or np.ndarray [shape=(n,)] or float scalar or vector of frequencies tuning : float Tuning deviation from A440 in (fractional) bins per octave. bins_per_octave : int > 0 Number of bins per octave. Returns ------- octaves : number or np.ndarray [shape=(n,)] octave number for each frequency See Also -------- octs_to_hz """A440=440.0*2.0**(tuning/bins_per_octave)octs:np.ndarray=np.log2(np.asanyarray(frequencies)/(float(A440)/16))returnocts
[docs]defocts_to_hz(octs:_ScalarOrSequence[_FloatLike_co],*,tuning:float=0.0,bins_per_octave:int=12,)->Union[np.floating[Any],np.ndarray]:"""Convert octaves numbers to frequencies. Octaves are counted relative to A. Examples -------- >>> librosa.octs_to_hz(1) 55. >>> librosa.octs_to_hz([-2, -1, 0, 1, 2]) array([ 6.875, 13.75 , 27.5 , 55. , 110. ]) Parameters ---------- octs : np.ndarray [shape=(n,)] or float octave number for each frequency tuning : float Tuning deviation from A440 in (fractional) bins per octave. bins_per_octave : int > 0 Number of bins per octave. Returns ------- frequencies : number or np.ndarray [shape=(n,)] scalar or vector of frequencies See Also -------- hz_to_octs """A440=440.0*2.0**(tuning/bins_per_octave)return(float(A440)/16)*(2.0**np.asanyarray(octs))
[docs]defA4_to_tuning(A4:_ScalarOrSequence[_FloatLike_co],*,bins_per_octave:int=12)->Union[np.floating[Any],np.ndarray]:"""Convert a reference pitch frequency (e.g., ``A4=435``) to a tuning estimation, in fractions of a bin per octave. This is useful for determining the tuning deviation relative to A440 of a given frequency, assuming equal temperament. By default, 12 bins per octave are used. This method is the inverse of `tuning_to_A4`. Examples -------- The base case of this method in which A440 yields 0 tuning offset from itself. >>> librosa.A4_to_tuning(440.0) 0. Convert a non-A440 frequency to a tuning offset relative to A440 using the default of 12 bins per octave. >>> librosa.A4_to_tuning(432.0) -0.318 Convert two reference pitch frequencies to corresponding tuning estimations at once, but using 24 bins per octave. >>> librosa.A4_to_tuning([440.0, 444.0], bins_per_octave=24) array([ 0., 0.313 ]) Parameters ---------- A4 : float or np.ndarray [shape=(n,), dtype=float] Reference frequency(s) corresponding to A4. bins_per_octave : int > 0 Number of bins per octave. Returns ------- tuning : float or np.ndarray [shape=(n,), dtype=float] Tuning deviation from A440 in (fractional) bins per octave. See Also -------- tuning_to_A4 """tuning:np.ndarray=bins_per_octave*(np.log2(np.asanyarray(A4))-np.log2(440.0))returntuning
[docs]deftuning_to_A4(tuning:_ScalarOrSequence[_FloatLike_co],*,bins_per_octave:int=12)->Union[np.floating[Any],np.ndarray]:"""Convert a tuning deviation (from 0) in fractions of a bin per octave (e.g., ``tuning=-0.1``) to a reference pitch frequency relative to A440. This is useful if you are working in a non-A440 tuning system to determine the reference pitch frequency given a tuning offset and assuming equal temperament. By default, 12 bins per octave are used. This method is the inverse of `A4_to_tuning`. Examples -------- The base case of this method in which a tuning deviation of 0 gets us to our A440 reference pitch. >>> librosa.tuning_to_A4(0.0) 440. Convert a nonzero tuning offset to its reference pitch frequency. >>> librosa.tuning_to_A4(-0.318) 431.992 Convert 3 tuning deviations at once to respective reference pitch frequencies, using 36 bins per octave. >>> librosa.tuning_to_A4([0.1, 0.2, -0.1], bins_per_octave=36) array([ 440.848, 441.698 439.154]) Parameters ---------- tuning : float or np.ndarray [shape=(n,), dtype=float] Tuning deviation from A440 in fractional bins per octave. bins_per_octave : int > 0 Number of bins per octave. Returns ------- A4 : float or np.ndarray [shape=(n,), dtype=float] Reference frequency corresponding to A4. See Also -------- A4_to_tuning """return440.0*2.0**(np.asanyarray(tuning)/bins_per_octave)
[docs]defcqt_frequencies(n_bins:int,*,fmin:float,bins_per_octave:int=12,tuning:float=0.0)->np.ndarray:"""Compute the center frequencies of Constant-Q bins. Examples -------- >>> # Get the CQT frequencies for 24 notes, starting at C2 >>> librosa.cqt_frequencies(24, fmin=librosa.note_to_hz('C2')) array([ 65.406, 69.296, 73.416, 77.782, 82.407, 87.307, 92.499, 97.999, 103.826, 110. , 116.541, 123.471, 130.813, 138.591, 146.832, 155.563, 164.814, 174.614, 184.997, 195.998, 207.652, 220. , 233.082, 246.942]) Parameters ---------- n_bins : int > 0 [scalar] Number of constant-Q bins fmin : float > 0 [scalar] Minimum frequency bins_per_octave : int > 0 [scalar] Number of bins per octave tuning : float Deviation from A440 tuning in fractional bins Returns ------- frequencies : np.ndarray [shape=(n_bins,)] Center frequency for each CQT bin """correction:float=2.0**(float(tuning)/bins_per_octave)frequencies:np.ndarray=2.0**(np.arange(0,n_bins,dtype=float)/bins_per_octave)returncorrection*fmin*frequencies
[docs]defmel_frequencies(n_mels:int=128,*,fmin:float=0.0,fmax:float=11025.0,htk:bool=False)->np.ndarray:"""Compute an array of acoustic frequencies tuned to the mel scale. The mel scale is a quasi-logarithmic function of acoustic frequency designed such that perceptually similar pitch intervals (e.g. octaves) appear equal in width over the full hearing range. Because the definition of the mel scale is conditioned by a finite number of subjective psychoaoustical experiments, several implementations coexist in the audio signal processing literature [#]_. By default, librosa replicates the behavior of the well-established MATLAB Auditory Toolbox of Slaney [#]_. According to this default implementation, the conversion from Hertz to mel is linear below 1 kHz and logarithmic above 1 kHz. Another available implementation replicates the Hidden Markov Toolkit [#]_ (HTK) according to the following formula:: mel = 2595.0 * np.log10(1.0 + f / 700.0). The choice of implementation is determined by the ``htk`` keyword argument: setting ``htk=False`` leads to the Auditory toolbox implementation, whereas setting it ``htk=True`` leads to the HTK implementation. .. [#] Umesh, S., Cohen, L., & Nelson, D. Fitting the mel scale. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, pp. 217-220, 1998. .. [#] Slaney, M. Auditory Toolbox: A MATLAB Toolbox for Auditory Modeling Work. Technical Report, version 2, Interval Research Corporation, 1998. .. [#] Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., & Woodland, P. The HTK book, version 3.4. Cambridge University, March 2009. See Also -------- hz_to_mel mel_to_hz librosa.feature.melspectrogram librosa.feature.mfcc Parameters ---------- n_mels : int > 0 [scalar] Number of mel bins. fmin : float >= 0 [scalar] Minimum frequency (Hz). fmax : float >= 0 [scalar] Maximum frequency (Hz). htk : bool If True, use HTK formula to convert Hz to mel. Otherwise (False), use Slaney's Auditory Toolbox. Returns ------- bin_frequencies : ndarray [shape=(n_mels,)] Vector of ``n_mels`` frequencies in Hz which are uniformly spaced on the Mel axis. Examples -------- >>> librosa.mel_frequencies(n_mels=40) array([ 0. , 85.317, 170.635, 255.952, 341.269, 426.586, 511.904, 597.221, 682.538, 767.855, 853.173, 938.49 , 1024.856, 1119.114, 1222.042, 1334.436, 1457.167, 1591.187, 1737.532, 1897.337, 2071.84 , 2262.393, 2470.47 , 2697.686, 2945.799, 3216.731, 3512.582, 3835.643, 4188.417, 4573.636, 4994.285, 5453.621, 5955.205, 6502.92 , 7101.009, 7754.107, 8467.272, 9246.028, 10096.408, 11025. ]) """# 'Center freqs' of mel bands - uniformly spaced between limitsmin_mel=hz_to_mel(fmin,htk=htk)max_mel=hz_to_mel(fmax,htk=htk)mels=np.linspace(min_mel,max_mel,n_mels)hz:np.ndarray=mel_to_hz(mels,htk=htk)returnhz
[docs]deftempo_frequencies(n_bins:int,*,hop_length:int=512,sr:float=22050)->np.ndarray:"""Compute the frequencies (in beats per minute) corresponding to an onset auto-correlation or tempogram matrix. Parameters ---------- n_bins : int > 0 The number of lag bins hop_length : int > 0 The number of samples between each bin sr : number > 0 The audio sampling rate Returns ------- bin_frequencies : ndarray [shape=(n_bins,)] vector of bin frequencies measured in BPM. .. note:: ``bin_frequencies[0] = +np.inf`` corresponds to 0-lag Examples -------- Get the tempo frequencies corresponding to a 384-bin (8-second) tempogram >>> librosa.tempo_frequencies(384, sr=22050) array([ inf, 2583.984, 1291.992, ..., 6.782, 6.764, 6.747]) """bin_frequencies=np.zeros(int(n_bins),dtype=np.float64)bin_frequencies[0]=np.infbin_frequencies[1:]=60.0*sr/(hop_length*np.arange(1.0,n_bins))returnbin_frequencies
[docs]deffourier_tempo_frequencies(*,sr:float=22050,win_length:int=384,hop_length:int=512)->np.ndarray:"""Compute the frequencies (in beats per minute) corresponding to a Fourier tempogram matrix. Parameters ---------- sr : number > 0 The audio sampling rate win_length : int > 0 The number of frames per analysis window hop_length : int > 0 The number of samples between each bin Returns ------- bin_frequencies : ndarray [shape=(win_length // 2 + 1 ,)] vector of bin frequencies measured in BPM. Examples -------- Get the tempo frequencies corresponding to a 384-bin (8-second) tempogram >>> librosa.fourier_tempo_frequencies(win_length=384, sr=22050) array([ 0. , 0.117, 0.234, ..., 22.266, 22.383, 22.5 ]) """# sr / hop_length gets the frame rate# multiplying by 60 turns frames / sec into frames / minutereturnfft_frequencies(sr=sr*60/float(hop_length),n_fft=win_length)
# A-weighting should be capitalized: suppress the naming warning@overloaddefA_weighting(frequencies:_FloatLike_co,*,min_db:Optional[float]=...)->np.floating[Any]:# pylint: disable=invalid-name...@overloaddefA_weighting(frequencies:_SequenceLike[_FloatLike_co],*,min_db:Optional[float]=...)->np.ndarray:# pylint: disable=invalid-name...@overloaddefA_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,min_db:Optional[float]=...)->Union[np.floating[Any],np.ndarray]:# pylint: disable=invalid-name...
[docs]defA_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,min_db:Optional[float]=-80.0)->Union[np.floating[Any],np.ndarray]:# pylint: disable=invalid-name"""Compute the A-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- A_weighting : scalar or np.ndarray [shape=(n,)] ``A_weighting[i]`` is the A-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting B_weighting C_weighting D_weighting Examples -------- Get the A-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.A_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ... ylabel='Weighting (log10)', ... title='A-Weighting of CQT frequencies') """f_sq=np.asanyarray(frequencies)**2.0const=np.array([12194.217,20.598997,107.65265,737.86223])**2.0weights:np.ndarray=2.0+20.0*(np.log10(const[0])+2*np.log10(f_sq)-np.log10(f_sq+const[0])-np.log10(f_sq+const[1])-0.5*np.log10(f_sq+const[2])-0.5*np.log10(f_sq+const[3]))ifmin_dbisNone:returnweightselse:returnnp.maximum(min_db,weights)
[docs]defB_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,min_db:Optional[float]=-80.0)->Union[np.floating[Any],np.ndarray]:# pylint: disable=invalid-name"""Compute the B-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- B_weighting : scalar or np.ndarray [shape=(n,)] ``B_weighting[i]`` is the B-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting C_weighting D_weighting Examples -------- Get the B-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.B_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ... ylabel='Weighting (log10)', ... title='B-Weighting of CQT frequencies') """f_sq=np.asanyarray(frequencies)**2.0const=np.array([12194.217,20.598997,158.48932])**2.0weights:np.ndarray=0.17+20.0*(np.log10(const[0])+1.5*np.log10(f_sq)-np.log10(f_sq+const[0])-np.log10(f_sq+const[1])-0.5*np.log10(f_sq+const[2]))returnweightsifmin_dbisNoneelsenp.maximum(min_db,weights)
[docs]defC_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,min_db:Optional[float]=-80.0)->Union[np.floating[Any],np.ndarray]:# pylint: disable=invalid-name"""Compute the C-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- C_weighting : scalar or np.ndarray [shape=(n,)] ``C_weighting[i]`` is the C-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting B_weighting D_weighting Examples -------- Get the C-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.C_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='C-Weighting of CQT frequencies') """f_sq=np.asanyarray(frequencies)**2.0const=np.array([12194.217,20.598997])**2.0weights:np.ndarray=0.062+20.0*(np.log10(const[0])+np.log10(f_sq)-np.log10(f_sq+const[0])-np.log10(f_sq+const[1]))returnweightsifmin_dbisNoneelsenp.maximum(min_db,weights)
[docs]defD_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,min_db:Optional[float]=-80.0)->Union[np.floating[Any],np.ndarray]:# pylint: disable=invalid-name"""Compute the D-weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- D_weighting : scalar or np.ndarray [shape=(n,)] ``D_weighting[i]`` is the D-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting B_weighting C_weighting Examples -------- Get the D-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.D_weighting(freqs) >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='D-Weighting of CQT frequencies') """f_sq=np.asanyarray(frequencies)**2.0const=np.array([8.3046305e-3,1018.7,1039.6,3136.5,3424,282.7,1160])**2.0weights:np.ndarray=20.0*(0.5*np.log10(f_sq)-np.log10(const[0])+0.5*(+np.log10((const[1]-f_sq)**2+const[2]*f_sq)-np.log10((const[3]-f_sq)**2+const[4]*f_sq)-np.log10(const[5]+f_sq)-np.log10(const[6]+f_sq)))ifmin_dbisNone:returnweightselse:returnnp.maximum(min_db,weights)
defZ_weighting(frequencies:Sized,*,min_db:Optional[float]=None)->np.ndarray:# pylint: disable=invalid-name"""Apply no weighting curve (aka Z-weighting). This function behaves similarly to `A_weighting`, `B_weighting`, etc., but all frequencies are equally weighted. An optional threshold `min_db` can still be used to clip energies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) min_db : float [scalar] or None Clip weights below this threshold. If `None`, no clipping is performed. Returns ------- Z_weighting : scalar or np.ndarray [shape=(n,)] ``Z_weighting[i]`` is the Z-weighting of ``frequencies[i]`` See Also -------- perceptual_weighting frequency_weighting multi_frequency_weighting A_weighting B_weighting C_weighting D_weighting """weights=np.zeros(len(frequencies))ifmin_dbisNone:returnweightselse:returnnp.maximum(min_db,weights)WEIGHTING_FUNCTIONS:Dict[Optional[str],Callable[...,Union[np.floating[Any],np.ndarray]]]={"A":A_weighting,"B":B_weighting,"C":C_weighting,"D":D_weighting,"Z":Z_weighting,None:Z_weighting,}@overloaddeffrequency_weighting(frequencies:_FloatLike_co,*,kind:str=...,**kwargs:Any)->np.floating[Any]:# pylint: disable=invalid-name...@overloaddeffrequency_weighting(frequencies:_SequenceLike[_FloatLike_co],*,kind:str=...,**kwargs:Any)->np.ndarray:# pylint: disable=invalid-name...@overloaddeffrequency_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,kind:str=...,**kwargs:Any)->Union[np.floating[Any],np.ndarray]:# pylint: disable=invalid-name...
[docs]deffrequency_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,kind:str="A",**kwargs:Any)->Union[np.floating[Any],np.ndarray]:"""Compute the weighting of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) kind : str in The weighting kind. e.g. `'A'`, `'B'`, `'C'`, `'D'`, `'Z'` **kwargs Additional keyword arguments to A_weighting, B_weighting, etc. Returns ------- weighting : scalar or np.ndarray [shape=(n,)] ``weighting[i]`` is the weighting of ``frequencies[i]`` See Also -------- perceptual_weighting multi_frequency_weighting A_weighting B_weighting C_weighting D_weighting Examples -------- Get the A-weighting for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weights = librosa.frequency_weighting(freqs, kind='A') >>> fig, ax = plt.subplots() >>> ax.plot(freqs, weights) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='A-Weighting of CQT frequencies') """ifisinstance(kind,str):kind=kind.upper()returnWEIGHTING_FUNCTIONS[kind](frequencies,**kwargs)
[docs]defmulti_frequency_weighting(frequencies:_ScalarOrSequence[_FloatLike_co],*,kinds:Iterable[str]="ZAC",**kwargs:Any,)->np.ndarray:"""Compute multiple weightings of a set of frequencies. Parameters ---------- frequencies : scalar or np.ndarray [shape=(n,)] One or more frequencies (in Hz) kinds : list or tuple or str An iterable of weighting kinds. e.g. `('Z', 'B')`, `'ZAD'`, `'C'` **kwargs : keywords to pass to the weighting function. Returns ------- weighting : scalar or np.ndarray [shape=(len(kinds), n)] ``weighting[i, j]`` is the weighting of ``frequencies[j]`` using the curve determined by ``kinds[i]``. See Also -------- perceptual_weighting frequency_weighting A_weighting B_weighting C_weighting D_weighting Examples -------- Get the A, B, C, D, and Z weightings for CQT frequencies >>> import matplotlib.pyplot as plt >>> freqs = librosa.cqt_frequencies(n_bins=108, fmin=librosa.note_to_hz('C1')) >>> weightings = 'ABCDZ' >>> weights = librosa.multi_frequency_weighting(freqs, kinds=weightings) >>> fig, ax = plt.subplots() >>> for label, w in zip(weightings, weights): ... ax.plot(freqs, w, label=label) >>> ax.set(xlabel='Frequency (Hz)', ylabel='Weighting (log10)', ... title='Weightings of CQT frequencies') >>> ax.legend() """returnnp.stack([frequency_weighting(frequencies,kind=k,**kwargs)forkinkinds],axis=0)
[docs]deftimes_like(X:Union[np.ndarray,float],*,sr:float=22050,hop_length:int=512,n_fft:Optional[int]=None,axis:int=-1,)->np.ndarray:"""Return an array of time values to match the time axis from a feature matrix. Parameters ---------- X : np.ndarray or scalar - If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram. - If scalar, X represents the number of frames. sr : number > 0 [scalar] audio sampling rate hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. axis : int [scalar] The axis representing the time axis of X. By default, the last axis (-1) is taken. Returns ------- times : np.ndarray [shape=(n,)] ndarray of times (in seconds) corresponding to each frame of X. See Also -------- samples_like : Return an array of sample indices to match the time axis from a feature matrix. Examples -------- Provide a feature matrix input: >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> D = librosa.stft(y) >>> times = librosa.times_like(D, sr=sr) >>> times array([0. , 0.023, ..., 5.294, 5.317]) Provide a scalar input: >>> n_frames = 2647 >>> times = librosa.times_like(n_frames, sr=sr) >>> times array([ 0.00000000e+00, 2.32199546e-02, 4.64399093e-02, ..., 6.13935601e+01, 6.14167800e+01, 6.14400000e+01]) """samples=samples_like(X,hop_length=hop_length,n_fft=n_fft,axis=axis)time:np.ndarray=samples_to_time(samples,sr=sr)returntime
[docs]defsamples_like(X:Union[np.ndarray,float],*,hop_length:int=512,n_fft:Optional[int]=None,axis:int=-1,)->np.ndarray:"""Return an array of sample indices to match the time axis from a feature matrix. Parameters ---------- X : np.ndarray or scalar - If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram. - If scalar, X represents the number of frames. hop_length : int > 0 [scalar] number of samples between successive frames n_fft : None or int > 0 [scalar] Optional: length of the FFT window. If given, time conversion will include an offset of ``n_fft // 2`` to counteract windowing effects when using a non-centered STFT. axis : int [scalar] The axis representing the time axis of ``X``. By default, the last axis (-1) is taken. Returns ------- samples : np.ndarray [shape=(n,)] ndarray of sample indices corresponding to each frame of ``X``. See Also -------- times_like : Return an array of time values to match the time axis from a feature matrix. Examples -------- Provide a feature matrix input: >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> X = librosa.stft(y) >>> samples = librosa.samples_like(X) >>> samples array([ 0, 512, ..., 116736, 117248]) Provide a scalar input: >>> n_frames = 2647 >>> samples = librosa.samples_like(n_frames) >>> samples array([ 0, 512, 1024, ..., 1353728, 1354240, 1354752]) """# suppress type checks because mypy does not understand isscalarifnp.isscalar(X):frames=np.arange(X)# type: ignoreelse:frames=np.arange(X.shape[axis])# type: ignorereturnframes_to_samples(frames,hop_length=hop_length,n_fft=n_fft)
[docs]@vectorize(excluded=["Sa","abbr","octave","unicode"])defmidi_to_svara_h(midi:Union[_FloatLike_co,np.ndarray],*,Sa:_FloatLike_co,abbr:bool=True,octave:bool=True,unicode:bool=True,)->Union[str,np.ndarray]:"""Convert MIDI numbers to Hindustani svara Parameters ---------- midi : numeric or np.ndarray The MIDI number or numbers to convert Sa : number > 0 MIDI number of the reference Sa. abbr : bool If `True` (default) return abbreviated names ('S', 'r', 'R', 'g', 'G', ...) If `False`, return long-form names ('Sa', 're', 'Re', 'ga', 'Ga', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given MIDI number(s) See Also -------- hz_to_svara_h note_to_svara_h midi_to_svara_c midi_to_note Examples -------- Convert a single midi number: >>> librosa.midi_to_svara_h(65, Sa=60) 'm' The first three svara with Sa at midi number 60: >>> librosa.midi_to_svara_h([60, 61, 62], Sa=60) array(['S', 'r', 'R'], dtype='<U1') With Sa=67, midi 60-62 are in the octave below: >>> librosa.midi_to_svara_h([60, 61, 62], Sa=67) array(['ṃ', 'Ṃ', 'P̣'], dtype='<U2') Or without unicode decoration: >>> librosa.midi_to_svara_h([60, 61, 62], Sa=67, unicode=False) array(['m,', 'M,', 'P,'], dtype='<U2') Or going up an octave, with Sa=60, and using unabbreviated notes >>> librosa.midi_to_svara_h([72, 73, 74], Sa=60, abbr=False) array(['Ṡa', 'ṙe', 'Ṙe'], dtype='<U3') """SVARA_MAP=["Sa","re","Re","ga","Ga","ma","Ma","Pa","dha","Dha","ni","Ni",]SVARA_MAP_SHORT=list(s[0]forsinSVARA_MAP)# mypy does not understand vectorizationsvara_num=int(np.round(midi-Sa))# type: ignoreifabbr:svara=SVARA_MAP_SHORT[svara_num%12]else:svara=SVARA_MAP[svara_num%12]ifoctave:if24>svara_num>=12:ifunicode:svara=svara[0]+"\u0307"+svara[1:]else:svara+="'"elif-12<=svara_num<0:ifunicode:svara=svara[0]+"\u0323"+svara[1:]else:svara+=","returnsvara
[docs]defhz_to_svara_h(frequencies:_ScalarOrSequence[_FloatLike_co],*,Sa:_FloatLike_co,abbr:bool=True,octave:bool=True,unicode:bool=True,)->Union[str,np.ndarray]:"""Convert frequencies (in Hz) to Hindustani svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- frequencies : positive number or np.ndarray The frequencies (in Hz) to convert Sa : positive number Frequency (in Hz) of the reference Sa. abbr : bool If `True` (default) return abbreviated names ('S', 'r', 'R', 'g', 'G', ...) If `False`, return long-form names ('Sa', 're', 'Re', 'ga', 'Ga', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given frequency/frequencies See Also -------- midi_to_svara_h note_to_svara_h hz_to_svara_c hz_to_note Examples -------- Convert Sa in three octaves: >>> librosa.hz_to_svara_h([261/2, 261, 261*2], Sa=261) ['Ṣ', 'S', 'Ṡ'] Convert one octave worth of frequencies with full names: >>> freqs = librosa.cqt_frequencies(n_bins=12, fmin=261) >>> librosa.hz_to_svara_h(freqs, Sa=freqs[0], abbr=False) ['Sa', 're', 'Re', 'ga', 'Ga', 'ma', 'Ma', 'Pa', 'dha', 'Dha', 'ni', 'Ni'] """midis=hz_to_midi(frequencies)returnmidi_to_svara_h(midis,Sa=hz_to_midi(Sa),abbr=abbr,octave=octave,unicode=unicode)
[docs]defnote_to_svara_h(notes:Union[str,_IterableLike[str]],*,Sa:str,abbr:bool=True,octave:bool=True,unicode:bool=True,)->Union[str,np.ndarray]:"""Convert western notes to Hindustani svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- notes : str or iterable of str Notes to convert (e.g., `'C#'` or `['C4', 'Db4', 'D4']` Sa : str Note corresponding to Sa (e.g., `'C'` or `'C5'`). If no octave information is provided, it will default to octave 0 (``C0`` ~= 16 Hz) abbr : bool If `True` (default) return abbreviated names ('S', 'r', 'R', 'g', 'G', ...) If `False`, return long-form names ('Sa', 're', 'Re', 'ga', 'Ga', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given notes See Also -------- midi_to_svara_h hz_to_svara_h note_to_svara_c note_to_midi note_to_hz Examples -------- >>> librosa.note_to_svara_h(['C4', 'G4', 'C5', 'G5'], Sa='C5') ['Ṣ', 'P̣', 'S', 'P'] """midis=note_to_midi(notes,round_midi=False)returnmidi_to_svara_h(midis,Sa=note_to_midi(Sa),abbr=abbr,octave=octave,unicode=unicode)
[docs]@vectorize(excluded=["Sa","mela","abbr","octave","unicode"])# type: ignoredefmidi_to_svara_c(midi:Union[float,np.ndarray],*,Sa:_FloatLike_co,mela:Union[int,str],abbr:bool=True,octave:bool=True,unicode:bool=True,)->Union[str,np.ndarray]:"""Convert MIDI numbers to Carnatic svara within a given melakarta raga Parameters ---------- midi : numeric The MIDI numbers to convert Sa : number > 0 MIDI number of the reference Sa. Default: 60 (261.6 Hz, `C4`) mela : int or str The name or index of the melakarta raga abbr : bool If `True` (default) return abbreviated names ('S', 'R1', 'R2', 'G1', 'G2', ...) If `False`, return long-form names ('Sa', 'Ri1', 'Ri2', 'Ga1', 'Ga2', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information and subscript numbers. If `False`, use low-order ASCII (' and ,) for octave decorations. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given MIDI number(s) See Also -------- hz_to_svara_c note_to_svara_c mela_to_degrees mela_to_svara list_mela """svara_num=int(np.round(midi-Sa))svara_map=notation.mela_to_svara(mela,abbr=abbr,unicode=unicode)svara=svara_map[svara_num%12]ifoctave:if24>svara_num>=12:ifunicode:svara=svara[0]+"\u0307"+svara[1:]else:svara+="'"elif-12<=svara_num<0:ifunicode:svara=svara[0]+"\u0323"+svara[1:]else:svara+=","returnsvara
[docs]defhz_to_svara_c(frequencies:Union[float,np.ndarray],*,Sa:float,mela:Union[int,str],abbr:bool=True,octave:bool=True,unicode:bool=True,)->Union[str,np.ndarray]:"""Convert frequencies (in Hz) to Carnatic svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- frequencies : positive number or np.ndarray The frequencies (in Hz) to convert Sa : positive number Frequency (in Hz) of the reference Sa. mela : int [1, 72] or string The melakarta raga to use. abbr : bool If `True` (default) return abbreviated names ('S', 'R1', 'R2', 'G1', 'G2', ...) If `False`, return long-form names ('Sa', 'Ri1', 'Ri2', 'Ga1', 'Ga2', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given frequency/frequencies See Also -------- note_to_svara_c midi_to_svara_c hz_to_svara_h hz_to_note list_mela Examples -------- Convert Sa in three octaves: >>> librosa.hz_to_svara_c([261/2, 261, 261*2], Sa=261, mela='kanakangi') ['Ṣ', 'S', 'Ṡ'] Convert one octave worth of frequencies using melakarta #36: >>> freqs = librosa.cqt_frequencies(n_bins=12, fmin=261) >>> librosa.hz_to_svara_c(freqs, Sa=freqs[0], mela=36) ['S', 'R₁', 'R₂', 'R₃', 'G₃', 'M₁', 'M₂', 'P', 'D₁', 'D₂', 'D₃', 'N₃'] """midis=hz_to_midi(frequencies)returnmidi_to_svara_c(midis,Sa=hz_to_midi(Sa),mela=mela,abbr=abbr,octave=octave,unicode=unicode)
[docs]defnote_to_svara_c(notes:Union[str,_IterableLike[str]],*,Sa:str,mela:Union[str,int],abbr:bool=True,octave:bool=True,unicode:bool=True,)->Union[str,np.ndarray]:"""Convert western notes to Carnatic svara Note that this conversion assumes 12-tone equal temperament. Parameters ---------- notes : str or iterable of str Notes to convert (e.g., `'C#'` or `['C4', 'Db4', 'D4']` Sa : str Note corresponding to Sa (e.g., `'C'` or `'C5'`). If no octave information is provided, it will default to octave 0 (``C0`` ~= 16 Hz) mela : str or int [1, 72] Melakarta raga name or index abbr : bool If `True` (default) return abbreviated names ('S', 'R1', 'R2', 'G1', 'G2', ...) If `False`, return long-form names ('Sa', 'Ri1', 'Ri2', 'Ga1', 'Ga2', ...) octave : bool If `True`, decorate svara in neighboring octaves with over- or under-dots. If `False`, ignore octave height information. unicode : bool If `True`, use unicode symbols to decorate octave information. If `False`, use low-order ASCII (' and ,) for octave decorations. This only takes effect if `octave=True`. Returns ------- svara : str or np.ndarray of str The svara corresponding to the given notes See Also -------- midi_to_svara_c hz_to_svara_c note_to_svara_h note_to_midi note_to_hz list_mela Examples -------- >>> librosa.note_to_svara_h(['C4', 'G4', 'C5', 'D5', 'G5'], Sa='C5', mela=1) ['Ṣ', 'P̣', 'S', 'G₁', 'P'] """midis=note_to_midi(notes,round_midi=False)returnmidi_to_svara_c(midis,Sa=note_to_midi(Sa),mela=mela,abbr=abbr,octave=octave,unicode=unicode)
[docs]defhz_to_fjs(frequencies:_ScalarOrSequence[_FloatLike_co],*,fmin:Optional[float]=None,unison:Optional[str]=None,unicode:bool=False,)->Union[str,np.ndarray]:"""Convert one or more frequencies (in Hz) from a just intonation scale to notes in FJS notation. Parameters ---------- frequencies : float or iterable of float Input frequencies, specified in Hz fmin : float (optional) The minimum frequency, corresponding to a unison note. If not provided, it will be inferred as `min(frequencies)` unison : str (optional) The name of the unison note. If not provided, it will be inferred as the scientific pitch notation name of `fmin`, that is, `hz_to_note(fmin)` unicode : bool If `True`, then unicode symbols are used for accidentals. If `False`, then low-order ASCII symbols are used for accidentals. Returns ------- notes : str or np.ndarray(dtype=str) ``notes[i]`` is the closest note name to ``frequency[i]`` (or ``frequency`` if the input is scalar) See Also -------- hz_to_note interval_to_fjs Examples -------- Get a single note name for a frequency, relative to A=55 Hz >>> librosa.hz_to_fjs(66, fmin=55, unicode=True) 'C₅' Get notation for a 5-limit frequency set starting at A=55 >>> freqs = librosa.interval_frequencies(24, intervals="ji5", fmin=55) >>> freqs array([ 55. , 58.667, 61.875, 66. , 68.75 , 73.333, 77.344, 82.5 , 88. , 91.667, 99. , 103.125, 110. , 117.333, 123.75 , 132. , 137.5 , 146.667, 154.687, 165. , 176. , 183.333, 198. , 206.25 ]) >>> librosa.hz_to_fjs(freqs, unicode=True) array(['A', 'B♭₅', 'B', 'C₅', 'C♯⁵', 'D', 'D♯⁵', 'E', 'F₅', 'F♯⁵', 'G₅', 'G♯⁵', 'A', 'B♭₅', 'B', 'C₅', 'C♯⁵', 'D', 'D♯⁵', 'E', 'F₅', 'F♯⁵', 'G₅', 'G♯⁵'], dtype='<U3') """iffminisNone:# mypy doesn't know that min can handle scalarsfmin=np.min(frequencies)# type: ignoreifunisonisNone:unison=hz_to_note(fmin,octave=False,unicode=False)ifnp.isscalar(frequencies):# suppress type check - mypy does not understand scalar checksintervals=frequencies/fmin# type: ignoreelse:intervals=np.asarray(frequencies)/fmin# mypy does not understand vectorizationreturnnotation.interval_to_fjs(intervals,unison=unison,unicode=unicode)# type: ignore