SpeechAugment

Motivation

AI algorithms are mostly data-driven, and the quality of the data determines the quality of the model to some extent. This leads to the inherent shortcoming of deep learning, and data augmentation is an effective way to solve this problem.

Methods

This repo supports audio data augmentations such as :

• reverberation
• background noise
• distortion
• packet loss simulation
• farfield effect
• speed perturbation

After those time domain augmentations, one can apply feature extraction step.

Installation

To install the released stable version, enter the REPL mode

] add SpeechAugment

or

Pkg.add("SpeechAugment")

To install the development version, enter the REPL mode

] add https://github.com/sonosole/SpeechAugment.jl.git

Example

using WAV
using SpeechAugment

# 1. read a wav file as a speech example
batchsize = 8;
data,fs = wavread("/XXPath/ASpeechExample.wav");

# 2. init all the augmentation functions you want
echo  = initAddEcho(fs, (0.05,0.4), (3.0,3.2,2.5,3.5,2.0,3.0));
noise = initAddNoise("XXPathFullOfNoiseWAVs", 2, (5,15));
clip  = initClipWav((0.5,2.0));
drop  = initDropWav(fs, (0.09,0.15));
far   = initFarfieldWav(fs, (0.4,0.9));
speed = initSpeedWav((0.8,1.2));

# 3. make a function list or array
fnlist = [echo noise clip drop far speed];

# 4. augment #batchSize audios
wavs = Vector(undef, batchsize)
for i = 1:batchsize
    wavs[i] = copy(data)
end
wavs = augmentWavs(fnlist, wavs)
for i = 1:batchsize
    wavwrite(wavs[i], "A$i.wav",Fs=16000,nbits=32)
end

# there is also a function called `augmentWav`
# it augments one audio into multiple audios.
audios = augmentWav(fnlist, data, batchsize)
for i = 1:batchsize
    wavwrite(audios[i], "B$i.wav",Fs=16000,nbits=32)
end

Function Parameter Introduction

initAddEcho(fs::Number, T₆₀Span::NTuple{2,Number}, roomSpan::NTuple{6,Number}) -> addecho(wav::Array)

• fs sampling rate
• T₆₀Span effective reverberation time e.g. (minT60, maxT60)
• roomSpan room size e.g. (MinL, MaxL, MinW, MaxW, MinH, MaxH)

initAddNoise(path::String, period::Int, dBSpan::NTuple{2,Number}) -> addnoise(speech::Array)

• path a path only full of noise WAVs
• period every #period it would change another noise wav.
• dBSpan span of SNR e.g. (mindB, maxdB)

initClipWav(clipSpan::NTuple{2,Number}) -> clipwav(wav::Array)

• clipSpan how much it would clip a wav e.g. (0.5,2.0)

initDropWav(fs::Real, ratioSpan::NTuple{2,Number}) -> dropwav(wav::Array)

• fs sampling rate
• ratioSpan span of droping ratio e.g. (0.02, 0.09). 1.0 is the uplimit.

initFarfieldWav(fs::Real, maxvalueSpan::NTuple{2,Number}) -> farfieldwav(wav::Array)

• fs sampling rate
• maxvalueSpan ranges from (0.0,1.0). Smaller means farther away. (0.2, 0.9) is recommended.

initSpeedWav(speedSpan::NTuple{2,Number}) -> speedwav(wav::Array)

• speedSpan range of speed perturbation. (0.85, 1.15) is recommended.

All the NTuple{2,Number} parameters should follow the small on the left and the big on the right i.e. (minvalue, maxvalue). To precisely control the extent of augmentation, the below functions could be used:

• addEcho
• addNoise
• clipWav
• dropWav
• farfieldWav
• speedWav

For details, check the documentation or enter the help?> mode.