| #!/usr/bin/env python3 |
| # |
| # Copyright (C) 2018 The Android Open Source Project |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); you may not |
| # use this file except in compliance with the License. You may obtain a copy of |
| # the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the |
| # License for the specific language governing permissions and limitations under |
| # the License. |
| |
| import logging |
| import numpy |
| import os |
| import scipy.io.wavfile as sciwav |
| |
| from acts_contrib.test_utils.coex.audio_capture_device import AudioCaptureBase |
| from acts_contrib.test_utils.coex.audio_capture_device import CaptureAudioOverAdb |
| from acts_contrib.test_utils.coex.audio_capture_device import CaptureAudioOverLocal |
| from acts_contrib.test_utils.audio_analysis_lib import audio_analysis |
| from acts_contrib.test_utils.audio_analysis_lib.check_quality import quality_analysis |
| |
| ANOMALY_DETECTION_BLOCK_SIZE = audio_analysis.ANOMALY_DETECTION_BLOCK_SIZE |
| ANOMALY_GROUPING_TOLERANCE = audio_analysis.ANOMALY_GROUPING_TOLERANCE |
| PATTERN_MATCHING_THRESHOLD = audio_analysis.PATTERN_MATCHING_THRESHOLD |
| ANALYSIS_FILE_TEMPLATE = "audio_analysis_%s.txt" |
| bits_per_sample = 32 |
| |
| |
| def get_audio_capture_device(ad, audio_params): |
| """Gets the device object of the audio capture device connected to server. |
| |
| The audio capture device returned is specified by the audio_params |
| within user_params. audio_params must specify a "type" field, that |
| is either "AndroidDevice" or "Local" |
| |
| Args: |
| ad: Android Device object. |
| audio_params: object containing variables to record audio. |
| |
| Returns: |
| Object of the audio capture device. |
| |
| Raises: |
| ValueError if audio_params['type'] is not "AndroidDevice" or |
| "Local". |
| """ |
| |
| if audio_params['type'] == 'AndroidDevice': |
| return CaptureAudioOverAdb(ad, audio_params) |
| |
| elif audio_params['type'] == 'Local': |
| return CaptureAudioOverLocal(audio_params) |
| |
| else: |
| raise ValueError('Unrecognized audio capture device ' |
| '%s' % audio_params['type']) |
| |
| |
| class FileNotFound(Exception): |
| """Raises Exception if file is not present""" |
| |
| |
| class AudioCaptureResult(AudioCaptureBase): |
| def __init__(self, path, audio_params=None): |
| """Initializes Audio Capture Result class. |
| |
| Args: |
| path: Path of audio capture result. |
| """ |
| super().__init__() |
| self.path = path |
| self.audio_params = audio_params |
| self.analysis_path = os.path.join(self.log_path, |
| ANALYSIS_FILE_TEMPLATE) |
| if self.audio_params: |
| self._trim_wave_file() |
| |
| def THDN(self, win_size=None, step_size=None, q=1, freq=None): |
| """Calculate THD+N value for most recently recorded file. |
| |
| Args: |
| win_size: analysis window size (must be less than length of |
| signal). Used with step size to analyze signal piece by |
| piece. If not specified, entire signal will be analyzed. |
| step_size: number of samples to move window per-analysis. If not |
| specified, entire signal will be analyzed. |
| q: quality factor for the notch filter used to remove fundamental |
| frequency from signal to isolate noise. |
| freq: the fundamental frequency to remove from the signal. If none, |
| the fundamental frequency will be determined using FFT. |
| Returns: |
| channel_results (list): THD+N value for each channel's signal. |
| List index corresponds to channel index. |
| """ |
| if not (win_size and step_size): |
| return audio_analysis.get_file_THDN(filename=self.path, |
| q=q, |
| freq=freq) |
| else: |
| return audio_analysis.get_file_max_THDN(filename=self.path, |
| step_size=step_size, |
| window_size=win_size, |
| q=q, |
| freq=freq) |
| |
| def detect_anomalies(self, |
| freq=None, |
| block_size=ANOMALY_DETECTION_BLOCK_SIZE, |
| threshold=PATTERN_MATCHING_THRESHOLD, |
| tolerance=ANOMALY_GROUPING_TOLERANCE): |
| """Detect anomalies in most recently recorded file. |
| |
| An anomaly is defined as a sample in a recorded sine wave that differs |
| by at least the value defined by the threshold parameter from a golden |
| generated sine wave of the same amplitude, sample rate, and frequency. |
| |
| Args: |
| freq (int|float): fundamental frequency of the signal. All other |
| frequencies are noise. If None, will be calculated with FFT. |
| block_size (int): the number of samples to analyze at a time in the |
| anomaly detection algorithm. |
| threshold (float): the threshold of the correlation index to |
| determine if two sample values match. |
| tolerance (float): the sample tolerance for anomaly time values |
| to be grouped as the same anomaly |
| Returns: |
| channel_results (list): anomaly durations for each channel's signal. |
| List index corresponds to channel index. |
| """ |
| return audio_analysis.get_file_anomaly_durations(filename=self.path, |
| freq=freq, |
| block_size=block_size, |
| threshold=threshold, |
| tolerance=tolerance) |
| |
| @property |
| def analysis_fileno(self): |
| """Returns the file number to dump audio analysis results.""" |
| counter = 0 |
| while os.path.exists(self.analysis_path % counter): |
| counter += 1 |
| return counter |
| |
| def audio_quality_analysis(self): |
| """Measures audio quality based on the audio file given as input. |
| |
| Returns: |
| analysis_path on success. |
| """ |
| analysis_path = self.analysis_path % self.analysis_fileno |
| if not os.path.exists(self.path): |
| raise FileNotFound("Recorded file not found") |
| try: |
| quality_analysis(filename=self.path, |
| output_file=analysis_path, |
| bit_width=bits_per_sample, |
| rate=self.audio_params["sample_rate"], |
| channel=self.audio_params["channel"], |
| spectral_only=False) |
| except Exception as err: |
| logging.exception("Failed to analyze raw audio: %s" % err) |
| return analysis_path |
| |
| def _trim_wave_file(self): |
| """Trim wave files. |
| |
| """ |
| original_record_file_name = 'original_' + os.path.basename(self.path) |
| original_record_file_path = os.path.join(os.path.dirname(self.path), |
| original_record_file_name) |
| os.rename(self.path, original_record_file_path) |
| fs, data = sciwav.read(original_record_file_path) |
| trim_start = self.audio_params['trim_start'] |
| trim_end = self.audio_params['trim_end'] |
| trim = numpy.array([[trim_start, trim_end]]) |
| trim = trim * fs |
| new_wave_file_list = [] |
| for elem in trim: |
| # To check start and end doesn't exceed raw data dimension |
| start_read = min(elem[0], data.shape[0] - 1) |
| end_read = min(elem[1], data.shape[0] - 1) |
| new_wave_file_list.extend(data[start_read:end_read]) |
| new_wave_file = numpy.array(new_wave_file_list) |
| |
| sciwav.write(self.path, fs, new_wave_file) |