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Diffstat (limited to 'third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/signal_processing.py')
| -rw-r--r-- | third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/signal_processing.py | 359 |
1 files changed, 359 insertions, 0 deletions
diff --git a/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/signal_processing.py b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/signal_processing.py new file mode 100644 index 0000000000..95e801903d --- /dev/null +++ b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/signal_processing.py @@ -0,0 +1,359 @@ +# Copyright (c) 2017 The WebRTC project authors. All Rights Reserved. +# +# Use of this source code is governed by a BSD-style license +# that can be found in the LICENSE file in the root of the source +# tree. An additional intellectual property rights grant can be found +# in the file PATENTS. All contributing project authors may +# be found in the AUTHORS file in the root of the source tree. +"""Signal processing utility module. +""" + +import array +import logging +import os +import sys +import enum + +try: + import numpy as np +except ImportError: + logging.critical('Cannot import the third-party Python package numpy') + sys.exit(1) + +try: + import pydub + import pydub.generators +except ImportError: + logging.critical('Cannot import the third-party Python package pydub') + sys.exit(1) + +try: + import scipy.signal + import scipy.fftpack +except ImportError: + logging.critical('Cannot import the third-party Python package scipy') + sys.exit(1) + +from . import exceptions + + +class SignalProcessingUtils(object): + """Collection of signal processing utilities. + """ + + @enum.unique + class MixPadding(enum.Enum): + NO_PADDING = 0 + ZERO_PADDING = 1 + LOOP = 2 + + def __init__(self): + pass + + @classmethod + def LoadWav(cls, filepath, channels=1): + """Loads wav file. + + Args: + filepath: path to the wav audio track file to load. + channels: number of channels (downmixing to mono by default). + + Returns: + AudioSegment instance. + """ + if not os.path.exists(filepath): + logging.error('cannot find the <%s> audio track file', filepath) + raise exceptions.FileNotFoundError() + return pydub.AudioSegment.from_file(filepath, + format='wav', + channels=channels) + + @classmethod + def SaveWav(cls, output_filepath, signal): + """Saves wav file. + + Args: + output_filepath: path to the wav audio track file to save. + signal: AudioSegment instance. + """ + return signal.export(output_filepath, format='wav') + + @classmethod + def CountSamples(cls, signal): + """Number of samples per channel. + + Args: + signal: AudioSegment instance. + + Returns: + An integer. + """ + number_of_samples = len(signal.get_array_of_samples()) + assert signal.channels > 0 + assert number_of_samples % signal.channels == 0 + return number_of_samples / signal.channels + + @classmethod + def GenerateSilence(cls, duration=1000, sample_rate=48000): + """Generates silence. + + This method can also be used to create a template AudioSegment instance. + A template can then be used with other Generate*() methods accepting an + AudioSegment instance as argument. + + Args: + duration: duration in ms. + sample_rate: sample rate. + + Returns: + AudioSegment instance. + """ + return pydub.AudioSegment.silent(duration, sample_rate) + + @classmethod + def GeneratePureTone(cls, template, frequency=440.0): + """Generates a pure tone. + + The pure tone is generated with the same duration and in the same format of + the given template signal. + + Args: + template: AudioSegment instance. + frequency: Frequency of the pure tone in Hz. + + Return: + AudioSegment instance. + """ + if frequency > template.frame_rate >> 1: + raise exceptions.SignalProcessingException('Invalid frequency') + + generator = pydub.generators.Sine(sample_rate=template.frame_rate, + bit_depth=template.sample_width * 8, + freq=frequency) + + return generator.to_audio_segment(duration=len(template), volume=0.0) + + @classmethod + def GenerateWhiteNoise(cls, template): + """Generates white noise. + + The white noise is generated with the same duration and in the same format + of the given template signal. + + Args: + template: AudioSegment instance. + + Return: + AudioSegment instance. + """ + generator = pydub.generators.WhiteNoise( + sample_rate=template.frame_rate, + bit_depth=template.sample_width * 8) + return generator.to_audio_segment(duration=len(template), volume=0.0) + + @classmethod + def AudioSegmentToRawData(cls, signal): + samples = signal.get_array_of_samples() + if samples.typecode != 'h': + raise exceptions.SignalProcessingException( + 'Unsupported samples type') + return np.array(signal.get_array_of_samples(), np.int16) + + @classmethod + def Fft(cls, signal, normalize=True): + if signal.channels != 1: + raise NotImplementedError('multiple-channel FFT not implemented') + x = cls.AudioSegmentToRawData(signal).astype(np.float32) + if normalize: + x /= max(abs(np.max(x)), 1.0) + y = scipy.fftpack.fft(x) + return y[:len(y) / 2] + + @classmethod + def DetectHardClipping(cls, signal, threshold=2): + """Detects hard clipping. + + Hard clipping is simply detected by counting samples that touch either the + lower or upper bound too many times in a row (according to `threshold`). + The presence of a single sequence of samples meeting such property is enough + to label the signal as hard clipped. + + Args: + signal: AudioSegment instance. + threshold: minimum number of samples at full-scale in a row. + + Returns: + True if hard clipping is detect, False otherwise. + """ + if signal.channels != 1: + raise NotImplementedError( + 'multiple-channel clipping not implemented') + if signal.sample_width != 2: # Note that signal.sample_width is in bytes. + raise exceptions.SignalProcessingException( + 'hard-clipping detection only supported for 16 bit samples') + samples = cls.AudioSegmentToRawData(signal) + + # Detect adjacent clipped samples. + samples_type_info = np.iinfo(samples.dtype) + mask_min = samples == samples_type_info.min + mask_max = samples == samples_type_info.max + + def HasLongSequence(vector, min_legth=threshold): + """Returns True if there are one or more long sequences of True flags.""" + seq_length = 0 + for b in vector: + seq_length = seq_length + 1 if b else 0 + if seq_length >= min_legth: + return True + return False + + return HasLongSequence(mask_min) or HasLongSequence(mask_max) + + @classmethod + def ApplyImpulseResponse(cls, signal, impulse_response): + """Applies an impulse response to a signal. + + Args: + signal: AudioSegment instance. + impulse_response: list or numpy vector of float values. + + Returns: + AudioSegment instance. + """ + # Get samples. + assert signal.channels == 1, ( + 'multiple-channel recordings not supported') + samples = signal.get_array_of_samples() + + # Convolve. + logging.info( + 'applying %d order impulse response to a signal lasting %d ms', + len(impulse_response), len(signal)) + convolved_samples = scipy.signal.fftconvolve(in1=samples, + in2=impulse_response, + mode='full').astype( + np.int16) + logging.info('convolution computed') + + # Cast. + convolved_samples = array.array(signal.array_type, convolved_samples) + + # Verify. + logging.debug('signal length: %d samples', len(samples)) + logging.debug('convolved signal length: %d samples', + len(convolved_samples)) + assert len(convolved_samples) > len(samples) + + # Generate convolved signal AudioSegment instance. + convolved_signal = pydub.AudioSegment(data=convolved_samples, + metadata={ + 'sample_width': + signal.sample_width, + 'frame_rate': + signal.frame_rate, + 'frame_width': + signal.frame_width, + 'channels': signal.channels, + }) + assert len(convolved_signal) > len(signal) + + return convolved_signal + + @classmethod + def Normalize(cls, signal): + """Normalizes a signal. + + Args: + signal: AudioSegment instance. + + Returns: + An AudioSegment instance. + """ + return signal.apply_gain(-signal.max_dBFS) + + @classmethod + def Copy(cls, signal): + """Makes a copy os a signal. + + Args: + signal: AudioSegment instance. + + Returns: + An AudioSegment instance. + """ + return pydub.AudioSegment(data=signal.get_array_of_samples(), + metadata={ + 'sample_width': signal.sample_width, + 'frame_rate': signal.frame_rate, + 'frame_width': signal.frame_width, + 'channels': signal.channels, + }) + + @classmethod + def MixSignals(cls, + signal, + noise, + target_snr=0.0, + pad_noise=MixPadding.NO_PADDING): + """Mixes `signal` and `noise` with a target SNR. + + Mix `signal` and `noise` with a desired SNR by scaling `noise`. + If the target SNR is +/- infinite, a copy of signal/noise is returned. + If `signal` is shorter than `noise`, the length of the mix equals that of + `signal`. Otherwise, the mix length depends on whether padding is applied. + When padding is not applied, that is `pad_noise` is set to NO_PADDING + (default), the mix length equals that of `noise` - i.e., `signal` is + truncated. Otherwise, `noise` is extended and the resulting mix has the same + length of `signal`. + + Args: + signal: AudioSegment instance (signal). + noise: AudioSegment instance (noise). + target_snr: float, numpy.Inf or -numpy.Inf (dB). + pad_noise: SignalProcessingUtils.MixPadding, default: NO_PADDING. + + Returns: + An AudioSegment instance. + """ + # Handle infinite target SNR. + if target_snr == -np.Inf: + # Return a copy of noise. + logging.warning('SNR = -Inf, returning noise') + return cls.Copy(noise) + elif target_snr == np.Inf: + # Return a copy of signal. + logging.warning('SNR = +Inf, returning signal') + return cls.Copy(signal) + + # Check signal and noise power. + signal_power = float(signal.dBFS) + noise_power = float(noise.dBFS) + if signal_power == -np.Inf: + logging.error('signal has -Inf power, cannot mix') + raise exceptions.SignalProcessingException( + 'cannot mix a signal with -Inf power') + if noise_power == -np.Inf: + logging.error('noise has -Inf power, cannot mix') + raise exceptions.SignalProcessingException( + 'cannot mix a signal with -Inf power') + + # Mix. + gain_db = signal_power - noise_power - target_snr + signal_duration = len(signal) + noise_duration = len(noise) + if signal_duration <= noise_duration: + # Ignore `pad_noise`, `noise` is truncated if longer that `signal`, the + # mix will have the same length of `signal`. + return signal.overlay(noise.apply_gain(gain_db)) + elif pad_noise == cls.MixPadding.NO_PADDING: + # `signal` is longer than `noise`, but no padding is applied to `noise`. + # Truncate `signal`. + return noise.overlay(signal, gain_during_overlay=gain_db) + elif pad_noise == cls.MixPadding.ZERO_PADDING: + # TODO(alessiob): Check that this works as expected. + return signal.overlay(noise.apply_gain(gain_db)) + elif pad_noise == cls.MixPadding.LOOP: + # `signal` is longer than `noise`, extend `noise` by looping. + return signal.overlay(noise.apply_gain(gain_db), loop=True) + else: + raise exceptions.SignalProcessingException('invalid padding type') |