Introduction

** OpenSMILE ** and ** Praat ** are famous as voice feature extraction software, but there are some parts that are a little difficult to handle due to the recent trend of deep learning. Therefore, this time, I will introduce Surfboard, which completes feature extraction on Python.

Although ** librosa ** and ** pysptk ** are famous as Python voice analysis libraries, this library has some features that are not included in them, so you can refer to them.

What is Surfboard?

Speech feature extraction library published by Novoic. It seems to be a company that mainly conducts research in the medical field. Paper: Surfboard: Audio Feature Extraction for Modern Machine Learning GitHub: https://github.com/novoic/surfboard

The abstract is quoted from the following paper.

We introduce Surfboard, an open-source Python library forextracting audio features with application to the medical do-main. Surfboard is written with the aim of addressing painpoints of existing libraries and facilitating joint use with mod-ern machine learning frameworks. The package can be accessedboth programmatically in Python and via its command line in-terface, allowing it to be easily integrated within machinelearn-ing workflows. It builds on state-of-the-art audio analysispack-ages and offers multiprocessing support for processing largeworkloads. We review similar frameworks and describe Surf-board’s architecture, including the clinical motivation for itsfeatures. Using the mPower dataset, we illustrate Surfboard’sapplication to a Parkinson’s disease classification task, high-lighting common pitfalls in existing research. The source codeis opened up to the research community to facilitate future audioresearch in the clinical domain.

Features provided by Surfboard

The figure is quoted from the paper. The leftmost Component column shows the features, and the second Impl. Column from the left shows the implementation source. Please refer to the paper for other information.

Operation procedure

Now, let's check how it is actually used or how easy it is to use.

Virtual environment recommended e.g. Virtualenv, Docker, Singularity, etc...

Execution environment

OS: Ubuntu 18.04 LTS CPU: i7-8700K CPU @ 3.70GHz Memory: 32GB Python version: 3.6.9 Surfboard version: 0.2.0

1. Install the required packages & libraries

sudo apt-get install libsndfile1-dev
pip3 install surfboard # latest version(2020/11/10 now 0.2.0）
pip3 install surfboard==0.2.0 #For version specification

2. Execution example

Here, the prepared methods are appropriately picked up and executed. The details of the features will not be explained here. (If you google it, it will come out a lot, so I'm feeling better now ... but I may add an explanation for the latter half of the execution example)

Read audio

surfboard.sound.Waveform(path=None, signal=None, sample_rate=44100)

>>> from surfboard.sound import Waveform
>>> sound = Waveform(path="input.wav", sample_rate=44100)
>>> sound
<surfboard.sound.Waveform object at 0x7f5d5a496630>

MFCC mfcc(n_mfcc=13, n_fft_seconds=0.04, hop_length_seconds=0.01)

>>> mfccs = sound.mfcc()
>>> mfccs
array([[-5.9755945e+02, -5.9204047e+02, -5.9595471e+02, ...,
        -5.9232190e+02, -6.0794177e+02, -6.8430023e+02],
       [ 1.0296422e+02,  1.0394064e+02,  9.9498421e+01, ...,
         1.0660390e+02,  1.1549076e+02,  8.2798584e+01],
       [ 3.3768288e+01,  3.1600494e+01,  3.0664955e+01, ...,
         1.9380785e+01,  1.6547699e+01,  2.9088814e+01],
       ...,
       [-1.5465357e+00, -6.0288420e+00, -7.3418264e+00, ...,
        -1.1875109e+01, -4.9084020e+00, -1.7681698e+00],
       [-2.0479255e+00, -6.1789474e+00, -4.2426043e+00, ...,
        -5.0735850e+00, -4.5268564e+00, -1.3781363e-01],
       [-9.6166210e+00, -1.5932017e+01, -8.2316790e+00, ...,
        -2.9154425e+00,  2.3177078e-01, -2.3197366e-02]], dtype=float32)
>>> mfccs.shape
(13, 251)

Log mel spectrogram log_melspec(n_mels=128, n_fft_seconds=0.04, hop_length_seconds=0.01)

>>> log_mel_spec = sound.log_melspec()
>>> log_mel_spec
array([[-44.61756 , -49.462692, -51.023216, ..., -53.39418 , -51.823517,
        -59.717033],
       [-47.49347 , -49.678703, -48.11801 , ..., -52.568924, -51.97367 ,
        -60.11091 ],
       [-62.110283, -49.851852, -46.291267, ..., -51.796555, -52.07287 ,
        -60.496307],
       ...,
       [-72.22576 , -74.225525, -75.74116 , ..., -80.      , -80.      ,
        -80.      ],
       [-75.85294 , -77.76551 , -75.66461 , ..., -80.      , -80.      ,
        -80.      ],
       [-77.79902 , -76.97334 , -76.3596  , ..., -80.      , -80.      ,
        -80.      ]], dtype=float32)
>>> log_mel_spec.shape
(128, 251)

Magnitude spectrum magnitude_spectrum(n_fft_seconds=0.04, hop_length_seconds=0.01)

>>> mag_spec = sound.magnitude_spectrum()
>>> mag_spec
array([[1.84691831e-01, 1.19033001e-01, 2.84719190e-05, ...,
        8.66711214e-02, 6.99108988e-02, 2.52321120e-02],
       [1.42162636e-01, 8.13821033e-02, 6.79990128e-02, ...,
        5.17552570e-02, 6.20137081e-02, 2.49926206e-02],
       [1.88411046e-02, 7.72730559e-02, 1.16427965e-01, ...,
        6.17721789e-02, 5.98379932e-02, 2.26884745e-02],
       ...,
       [1.00823166e-03, 9.13982571e-04, 9.08253191e-04, ...,
        1.17852981e-03, 7.16711569e-04, 2.36942826e-04],
       [2.61519599e-04, 9.38822108e-04, 3.16031976e-04, ...,
        4.44596924e-04, 6.49552734e-04, 2.54548184e-04],
       [7.14944093e-04, 8.84590496e-04, 1.49172614e-03, ...,
        9.47592314e-04, 8.35590414e-04, 7.54383451e-04]], dtype=float32)
>>> mag_spec.shape
(257, 251)

Shimmer shimmers(max_a_factor=1.6, p_floor=0.0001, p_ceil=0.02, max_p_factor=1.3)

>>> shimmers = sound.shimmers()
>>> shimmers
{'localShimmer': 0.12885916134577777, 'localdbShimmer': 0.7139508204315516, 'apq3Shimmer': 0.06556241042381906, 'apq5Shimmer': 0.23042452340218883, 'apq11Shimmer': 0.7440287411060755}

Jitter jitters(p_floor=0.0001, p_ceil=0.02, max_p_factor=1.3)

>>> jitters = sound.jitters()
>>> jitters
{'localJitter': 0.019661538782247998, 'localabsoluteJitter': 9.918096659472624e-05, 'rapJitter': 0.004236280859121549, 'ppq5Jitter': 0.007349282515463625, 'ddpJitter': 0.012708842577364697}

Formant formants()

>>> formants = sound.formants()
>>> formants
{'f1': 1598.7638894106142, 'f2': 2671.048125793429, 'f3': 3869.8610696529854, 'f4': 4427.027666549306}

Harmonics-to-Noise Ratio hnr()

>>> hnrs = sound.hnr()
>>> hnrs
3.412263760552974

Zero-crossing rate zerocrossing()

>>> zcrs = sound.zerocrossing()
>>> zcrs
{'num_zerocrossings': 3520, 'zerocrossing_rate': 0.10989010989010989}

If you want to know more details, please refer to the following page. https://surfboard.readthedocs.io/en/latest/waveform.html

Summary

--Introduced a voice feature extraction library called Surfboard and summarized how to use it easily. --Jitter and Shimmer were also OpenSMILE, but I thought it would be nice to be able to easily extract them as Python libraries. --However, note that the arguments of n_fft and hop_length are in units of ** seconds ** (librosa etc. are the number of samples). --There are many other methods available, so if you're interested, move your hand.

Reference site & paper

Praat: doing phonetics by computer praat : jitter OpenSMILE Rough memo of voice features for machine learning (Librosa, numpy) Speech Emotion Recognition with deep learning Speech Emotion Recognition Using Deep Neural Network and ExtremeLearning Machine

[PYTHON] Introduction of new voice feature extraction library Surfboard