Source code for compiam.melody.pitch_extraction.melodia

import os

import numpy as np

from compiam.utils.pitch import normalisation, resampling
from compiam.io import write_csv
from compiam.utils import get_logger

logger = get_logger(__name__)


[docs]class Melodia: """Melodia predominant melody extraction""" def __init__( self, bin_resolution=10, filter_iterations=3, frame_size=2048, guess_unvoiced=False, harmonic_weight=0.8, hop_size=128, magnitude_compression=1, magnitude_threshold=40, max_frequency=20000, min_duration=100, min_frequency=80, num_harmonics=20, peak_distribution_threshold=0.9, peak_frame_threshold=0.9, pitch_continuity=27.5625, reference_frequency=55, sample_rate=44100, time_continuity=100, voice_vibrato=False, voicing_tolerance=0.2, ): """Melodia predominant melody extraction init method For a complete and detailed list of the parameters see the documentation on the following link: https://essentia.upf.edu/reference/std_PredominantPitchMelodia.html """ ### IMPORTING OPTIONAL DEPENDENCIES try: global estd import essentia.standard as estd except: raise ImportError( "In order to use this tool you need to have essentia installed. " "Please install essentia using: pip install essentia" ) ### self.bin_resolution = bin_resolution self.filter_iterations = filter_iterations self.frame_size = frame_size self.guess_unvoiced = guess_unvoiced self.harmonic_weight = harmonic_weight self.hop_size = hop_size self.magnitude_compression = magnitude_compression self.magnitude_threshold = magnitude_threshold self.max_frequency = max_frequency self.min_duration = min_duration self.min_frequency = min_frequency self.num_harmonics = num_harmonics self.peak_distribution_threshold = peak_distribution_threshold self.peak_frame_threshold = peak_frame_threshold self.pitch_continuity = pitch_continuity self.reference_frequency = reference_frequency self.sample_rate = sample_rate self.time_continuity = time_continuity self.voice_vibrato = voice_vibrato self.voicing_tolerance = voicing_tolerance
[docs] def extract(self, input_data, input_sr=44100, out_step=None): """Extract the melody from a given file. :param input_data: path to audio file or numpy array like audio signal :param input_sr: sampling rate of the input array of data (if any). This variable is only relevant if the input is an array of data instead of a filepath. :param out_step: particular time-step duration if needed at output :returns: a 2-D list with time-stamps and pitch values per timestamp. """ if isinstance(input_data, str): if not os.path.exists(input_data): raise FileNotFoundError("Target audio not found.") audio = estd.EqloudLoader( filename=input_data, sampleRate=self.sample_rate )() elif isinstance(input_data, np.ndarray): logger.warning( f"Resampling... (input sampling rate is {input_sr}Hz, make sure this is correct)" ) resample_audio = estd.Resample( inputSampleRate=input_sr, outputSampleRate=self.sample_rate )() input_data = resample_audio(input_data) audio = estd.EqualLoudness(signal=input_data)() else: raise ValueError("Input must be path to audio signal or an audio array") extractor = estd.PredominantPitchMelodia( binResolution=self.bin_resolution, filterIterations=self.filter_iterations, frameSize=self.frame_size, guessUnvoiced=self.guess_unvoiced, harmonicWeight=self.harmonic_weight, hopSize=self.hop_size, magnitudeCompression=self.magnitude_compression, magnitudeThreshold=self.magnitude_threshold, maxFrequency=self.max_frequency, minDuration=self.min_duration, minFrequency=self.min_frequency, numberHarmonics=self.num_harmonics, peakDistributionThreshold=self.peak_distribution_threshold, peakFrameThreshold=self.peak_frame_threshold, pitchContinuity=self.pitch_continuity, referenceFrequency=self.reference_frequency, sampleRate=self.sample_rate, timeContinuity=self.time_continuity, voiceVibrato=self.voice_vibrato, voicingTolerance=self.voicing_tolerance, ) pitch, _ = extractor(audio) TStamps = ( np.array(range(0, len(pitch))) * float(self.hop_size) / self.sample_rate ) output = np.array([TStamps, pitch]).transpose() if out_step is not None: new_len = int((len(audio) / self.sample_rate) // out_step) return resampling(output, new_len) return output
[docs] @staticmethod def normalise_pitch(pitch, tonic, bins_per_octave=120, max_value=4): """Normalize pitch given a tonic. :param pitch: a 2-D list with time-stamps and pitch values per timestamp. :param tonic: recording tonic to normalize the pitch to. :param bins_per_octave: number of frequency bins per octave. :param max_value: maximum value to clip the normalized pitch to. :returns: a 2-D list with time-stamps and normalized to a given tonic pitch values per timestamp. """ return normalisation( pitch, tonic, bins_per_octave=bins_per_octave, max_value=max_value )
[docs] @staticmethod def save_pitch(data, output_path): """Calling the write_csv function in compiam.io to write the output pitch curve in a fle :param data: the data to write :param output_path: the path where the data is going to be stored :returns: None """ return write_csv(data, output_path)