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PacificSound2kHz

Copyright (c) 2021, MBARI

• Distributed under the terms of the GPL License
• Maintainer: dcline@mbari.org
• Authors: Danelle Cline dcline@mbari.org, John Ryan ryjo@mbari.org

Basic Exploration of the 2 kHz Pacific Ocean Audio Data in the AWS Open Data Registry¶

---

An extensive (5+ years and growing) archive of sound recordings from a deep-sea location along the eastern margin of the North Pacific Ocean has been made available through AWS Open data. Temporal coverage of the recording archive has been 95% since project inception in July 2015. The original recordings have a sample rate of 256 kHz. For many research applications it is convenient to work with data having a lower sample rate. This notebook illustrates basic methods to access and process a calibrated spectrogram from the decimated 2 kHz audio archive.

If you use this data set, please cite our project.

Data Overview¶

The decimated audio data are in WAV format in an s3 bucket named pacific-sound-2khz. They are further organized by year and month. Buckets are stored as objects, so the data isn't physically stored in folders or directories as you may be famaliar with, but you can think of it conceptually as follows:

pacific-sound-2khz
      |
      ----2020
        |
        |----01
        ...
        |----12

Install required dependencies¶

First, let's install the required software dependencies.

If you are using this notebook in a cloud environment, select a Python3 compatible kernel and run this next section. This only needs to be done once for the duration of this notebook.

If you are working on local computer, you can skip this next cell. Change your kernel to pacific-sound-notebooks, which you installed according to the instructions in the README - this has all the dependencies that are needed.

In [ ]:

!pip install -q boto3 --quiet
!pip install -q soundfile --quiet
!pip install -q scipy --quiet
!pip install -q numpy --quiet
!pip install -q matplotlib --quiet

!pip install -q boto3 --quiet !pip install -q soundfile --quiet !pip install -q scipy --quiet !pip install -q numpy --quiet !pip install -q matplotlib --quiet

Import all packages¶

In [ ]:

import boto3
from botocore import UNSIGNED
from botocore.client import Config
from six.moves.urllib.request import urlopen
import io
import scipy
from scipy import signal
import numpy as np
import soundfile as sf
import matplotlib.pyplot as plt

import boto3 from botocore import UNSIGNED from botocore.client import Config from six.moves.urllib.request import urlopen import io import scipy from scipy import signal import numpy as np import soundfile as sf import matplotlib.pyplot as plt

List the contents of a monthly directory¶

In [ ]:

s3 = boto3.client('s3',
    aws_access_key_id='',
    aws_secret_access_key='', 
    config=Config(signature_version=UNSIGNED))

s3 = boto3.client('s3', aws_access_key_id='', aws_secret_access_key='', config=Config(signature_version=UNSIGNED))

In [ ]:

year = 2020
month = 1
bucket = 'pacific-sound-2khz'

for obj in s3.list_objects_v2(Bucket=bucket, Prefix=f'{year:04d}/{month:02d}')['Contents']:
    print(obj['Key'])

year = 2020 month = 1 bucket = 'pacific-sound-2khz' for obj in s3.list_objects_v2(Bucket=bucket, Prefix=f'{year:04d}/{month:02d}')['Contents']: print(obj['Key'])

Retrieve metadata for a file¶

In [ ]:

year = 2020
month = 1
filename = 'MARS-20200101T000000Z-2kHz.wav'
bucket = 'pacific-sound-2khz'
key = f'{year:04d}/{month:02d}/{filename}'

url = f'https://{bucket}.s3.amazonaws.com/{key}'

sf.info(io.BytesIO(urlopen(url).read(20_000)), verbose=True)

year = 2020 month = 1 filename = 'MARS-20200101T000000Z-2kHz.wav' bucket = 'pacific-sound-2khz' key = f'{year:04d}/{month:02d}/{filename}' url = f'https://{bucket}.s3.amazonaws.com/{key}' sf.info(io.BytesIO(urlopen(url).read(20_000)), verbose=True)

Calibrated Spectrum Levels¶

Produce calibrated spectrogram for a full day¶

For the low-frequency (2 kHz) data, calibration data are not frequency dependent; a single low-frequency calibration value is used. Its value depends on time of data collection, as two hydrophones have been deployed sequentially at the same site. Before 14 June 2017, the calibration value is -168.8 dB re V / uPa (measured at 26 Hz). After this date the value is -177.9 dB re V / uPa (measured at 250 Hz). See also:

• https://bitbucket.org/mbari/pacific-sound/src/master/MBARI_MARS_Hydrophone_Deployment01.json
• https://bitbucket.org/mbari/pacific-sound/src/master/MBARI_MARS_Hydrophone_Deployment02.json

In [ ]:

# create a 1-Hz x n second calibrated spectrogram at 1 second resolution
print(f'Reading from {url}')
x, sample_rate = sf.read(io.BytesIO(urlopen(url).read()),dtype='float32') 
v = x*3   # convert scaled voltage to volts
v.shape, v.size, sample_rate
a = np.arange(v.size)+1
# define segment processing
nsec = (v.size)/sample_rate # number of seconds in vector
spa = 60  # seconds per average
nseg = int(nsec/spa)
print(f'{nseg} segments of length {spa} seconds in {nsec} seconds of audio')

# create a 1-Hz x n second calibrated spectrogram at 1 second resolution print(f'Reading from {url}') x, sample_rate = sf.read(io.BytesIO(urlopen(url).read()),dtype='float32') v = x*3 # convert scaled voltage to volts v.shape, v.size, sample_rate a = np.arange(v.size)+1 # define segment processing nsec = (v.size)/sample_rate # number of seconds in vector spa = 60 # seconds per average nseg = int(nsec/spa) print(f'{nseg} segments of length {spa} seconds in {nsec} seconds of audio')

In [ ]:

# initialize empty LTSA
nfreq = int(sample_rate/2+1)
nfreq,nseg
LTSA = np.empty((nfreq, nseg), float)
LTSA.shape

# initialize empty LTSA nfreq = int(sample_rate/2+1) nfreq,nseg LTSA = np.empty((nfreq, nseg), float) LTSA.shape

In [ ]:

# get window for welch
w = scipy.signal.get_window('hann',sample_rate)

# process LTSA
for x in range(0,nseg):
  cstart = x*spa*sample_rate
  cend = (x+1)*spa*sample_rate
  f,psd = scipy.signal.welch(v[cstart:cend],fs=sample_rate,window=w,nfft=sample_rate)
  psd = 10*np.log10(psd) + 177.9
  LTSA[:,x] = psd

# get window for welch w = scipy.signal.get_window('hann',sample_rate) # process LTSA for x in range(0,nseg): cstart = x*spa*sample_rate cend = (x+1)*spa*sample_rate f,psd = scipy.signal.welch(v[cstart:cend],fs=sample_rate,window=w,nfft=sample_rate) psd = 10*np.log10(psd) + 177.9 LTSA[:,x] = psd

In [ ]:

nsec, nseg, LTSA.shape

nsec, nseg, LTSA.shape

Plot the calibrated spectrogram¶

Note: The sharp drop in signal approaching 1 kHz reflects the attributes of the decimation filter applied to produce the 2 kHZ data from the original 256 kHz data.

In [ ]:

plt.figure(dpi=300)
im = plt.imshow(LTSA,aspect='auto',origin='lower',vmin=30,vmax=100)
plt.yscale('log')
plt.ylim(10,1000)
plt.colorbar(im)
plt.xlabel('Minute of day')
plt.ylabel('Frequency (Hz)')
plt.title('Calibrated spectrum levels')

plt.figure(dpi=300) im = plt.imshow(LTSA,aspect='auto',origin='lower',vmin=30,vmax=100) plt.yscale('log') plt.ylim(10,1000) plt.colorbar(im) plt.xlabel('Minute of day') plt.ylabel('Frequency (Hz)') plt.title('Calibrated spectrum levels')