Signal Processing for Deep Learning and Machine Learning · Signal Processing for Deep Learning and...

1© 2018 The MathWorks, Inc.

Signal Processing for Deep Learning and Machine

Learning

Kirthi Devleker,

Sr. Product Manager, Signal Processing and Wavelets

2

Key Topics

▪ Signal analysis and visualization

▪ Time-Frequency analysis techniques

▪ Signal Pre-processing and Feature Extraction

▪ Automating Signal Classification

3

Signals are Everywhere

▪ Structural health monitoring (SHM)

▪ Engine event detection

▪ Speech Signal Classification

▪ Advanced surveillance

▪ Healthcare Applications

▪ ...

4

You don’t have to be a signal processing engineer to

work with signals

You don’t have to be a data scientist to do machine

learning and deep learning

© 2018 The MathWorks, Inc.

Overview

Develop

Data

exploration

Preprocessing

Analyze Data

Domain-specific

algorithms

Sensors

Files

Access Data

Databases

Desktop apps

Enterprise

systems

Deploy

Embedded

devices

Model

Modeling &

simulation

Algorithm

development

6

Deep Learning Overview

What is Deep Learning ?

Deep Neural Networks

Feature Detection and Extraction Prediction

…….......

7

Inside a Deep Neural Network

8

Two Demos…

Automatic Feature Extraction using Wavelet

Scattering Framework

Music Genre RecognitionEKG Classification

Transfer Learning using AlexNet CNN

9

Approaches for Signal Classification

▪ Transfer Learning for Signal Classification

▪ Automate Feature Extraction using Wavelet Scattering

▪ Using LSTM networks

10





11

Example 1: Signal Classification using Transfer Learning

▪ Goal: Given a set of labeled signals, quickly build a

classifier

▪ Dataset: 160 records with ~65K samples each

– Normal (Class I)

– Atrial Fibrillation (Class II)

– Congestive Heart Failure (Class III)

▪ Approach: Pre-trained Models

– AlexNet

▪ Out of Scope: CNN architecture parameter tuning

12

Overall Workflow – Transfer Learning on Signals

Signals Time-Frequency

Representations

Train Transfer

Learning Model

Trained Model

New Signal PredictTime-Frequency

Representation

13

Benefits of Transfer Learning

▪ Reference models are great feature extractors

– Initial layers learn low level features like edges etc.

▪ Replace final layers

– New layers learn features specific to your data

▪ Good starting point

AlexNetPRETRAINED

MODEL

GoogLeNetPRETRAINED MODEL

VGG-16PRETRAINED

MODEL

14

Steps in Transfer Learning Workflow

Preprocess Data

Re-configure the Layers

Set training options

Train the network

Test/deploy trained network

Repeat these steps

until network reaches

desired level of

accuracy

15

Thinking about Layers

▪ Layers are like Lego Blocks

– Stack them on top of each other

– Easily replace one block with a different one

▪ Each hidden layer has a special function

that processes the information from the

previous layer

16

Convolutional Neural Networks (CNNs)

▪ Special layer combinations that make them great for classification

– Convolution Layer

– Max Pooling Layer

– ReLU Layer

17

Convolution Layers Search for Patterns

These patterns would be common in the number 0

18

All patterns are compared to the patterns on a

new image.

• Pattern starts at left corner

Perform comparison

Slide over one pixel

• Reach end of image

• Repeat for next pattern

…

19

Good pattern matching in convolution improves

chances that object will classify properly

▪ This image would not match

well against the patterns for the

number zero

▪ It would only do

very well against

this pattern

20

Max Pooling is a down-sampling operation

Reduce dimensionality while preserving important information

1 0 5 4

3 4 8 3

1 4 6 5

2 5 4 1

4 8

5 6

2x2 filters

Stride Length = 2

21

Rectified Linear Units Layer (ReLU)

Typically converts negative numbers to zero

-1 0 5 4

3 -4 -8 3

1 4 6 -5

-2 -5 4 1

0 0 5 4

3 0 0 3

1 4 6 0

0 0 4 1

22

CNNs typically end with 3 Layers

▪ Fully Connected Layer

– Looks at which high-level features correspond to a specific category

– Calculates scores for each category (highest score wins)

▪ Softmax Layer

– Turns scores into probabilities.

▪ Classification Layer

– Categorizes image into one of the classes that the network is trained on

23

Recap– Transfer Learning on Signals


Representation

Train Transfer

Learning Model

Trained Model


Representation

24

Converting signals to time-frequency representations

▪ A time-frequency representation captures how spectral content of signal

evolves over time

– This pattern can be saved as an image.

▪ Example techniques include:

– spectrogram, mel-frequency spectrograms,

– Constant-Q Transforms

– scalogram (continuous wavelet transform), (Sharp Time-Frequency Patterns)

▪ Recall: Convolution Layers search for patterns

– Having sharp time-frequency representations helps in training models quicker

– Sharp time-frequency representations can enhance subtle information within signals that

appear very similar but belong to different classes

25

What is a wavelet?

▪ A wavelet is a rapidly decaying wave like oscillation with zero

mean

▪ Wavelets are best suited to localize frequency content in real

world signals

▪ MATLAB makes it easy by providing default wavelets

Sine wave

Wavelet

26

Time-Frequency Analysis - Comparison

Short Time Fourier Transform

- Fixed window size limits the resolution

Continuous Wavelet Transform

- Wavelets – well localized in time and frequency

- Variable sized windows capture features at different

scales simultaneously

- No need for specifying window size / type etc.

2

27

Demo 1: EKG Classification

▪ Goal: Given a set of labeled signals, quickly build a

classifier

▪ Dataset: 160 records with ~65K samples each

– Normal (Class I)

– Atrial Fibrillation (Class II)

– Congestive Heart Failure (Class III)

▪ Approach: Pre-trained Models

– AlexNet

▪ Out of Scope: CNN architecture parameter tuning

28

Overall Workflow – Transfer Learning on Signals


Representation

Train Transfer

Learning Model

Trained Model

Wavelet based


Representation

Training

InferenceGenerate GPU Code

29

Let’s try it out!Exercise: DeepLearningForSignals.mlx

30





31

Example 2: Music Genre Recognition using Wavelet Scattering

▪ Dataset: GTZAN Genre Classification[1]

▪ Approach: Automatic Feature Extraction using

Wavelet Scattering

▪ Key Benefits:

– No guess work involved (hyper parameter tuning etc.)

– Automatically extract relevant features ➔ 2 lines

[1] Tzanetakis, G. and Cook, P. 2002. Music genre classification of audio signals. IEEE Transactions on Speech and Audio

Processing, Vol. 10, No. 5, pp. 293-302.

http://marsyasweb.appspot.com/download/data_sets/

32

▪ Initial activations of some well trained CNNs resemble wavelet like filters

▪ Introducing Wavelet Scattering Framework [1]

– Automatic Feature Extraction

– Great starting point if you don’t have a lot of data

– Reduces data dimensionality and provides compact features

Class - I

Class - II

.

.

.

Background

[1] Joan Bruna, and Stephane Mallat, P. 2013. Invariant Scattering Convolution Networks. IEEE Transactions on Pattern

Analysis and Machine Intelligence, Vol. 35, No. 8, pp. 1872-1886.

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34

33

Working -Wavelet Scattering

Machine Learning

Wavelet

Scattering

Framework

FeaturesSignal

Min. Signal

Length

Classifier

34

More Info on Scattering Framework

▪ Q: What is a deep network ?

A: A network that does:

Convolution ➔ Filter signal with wavelets

Non-Linearity ➔ Modulus

Averaging ➔ Filter with Scaling function

▪ A comparison:

Wavelet Scattering Framework Convolutional Neural Network

Outputs at every layer Output at the end

Fixed filter weights Filter weights are learnt

35

||F *1|* 12| ||F *1|* 11|

|F *n| |F *2| |F *1|

Inner Workings: Wavelet Scattering Framework

F

Wavelet Filter

Scaling Filter

…….

|F *1|*|F *2|*

Layer 1

Layer 2

F *

|F *n|*

…

……

Layer 3

||F *1|* 11|*

||F *1|* 12|*…

Scattering Coefficients (S)

…

Scalogram Coefficients (U)

36

Wavelet Scattering Workflow

Create Scattering Filterbank

Automatically Extract Features

Train any classifier with features

Test/deploy

Only 2 Lines

37

Let’s try it out!

38





39

Deep Learning with LSTMs - Examples

▪ Sequence Classification Using Deep Learning

▪ This example shows how to classify sequence data using a long short-

term memory (LSTM) network.

▪ Sequence-to-Sequence Classification Using Deep Learning

▪ This example shows how to classify each time step of sequence data

using a long short-term memory (LSTM) network.

▪ Sequence-to-Sequence Regression Using Deep Learning

▪ This example shows how to predict the remaining useful life (RUL) of

engines by using deep learning.

▪ …and many more

https://www.mathworks.com/help/deeplearning/examples/classify-sequence-data-using-lstm-networks.htmlhttps://www.mathworks.com/help/deeplearning/examples/sequence-to-sequence-classification-using-deep-learning.htmlhttps://www.mathworks.com/help/deeplearning/examples/sequence-to-sequence-regression-using-deep-learning.html

40

Signal Pre-processing / Feature Extraction

▪ Signal Pre-processing

– Wavelet Signal Denoiser

▪ Changepoint Detection

▪ Compare signals using Dynamic Time

Warping

▪ Reconstruct missing samples

▪ … ..

41

Leverage built-in algorithms

How much have I not needed to re-invent?

▪ Signal Processing Toolbox

▪ Wavelet Toolbox

▪ Deep Learning Toolbox

▪ Statistics and Machine Learning Toolbox

▪ Parallel Computing Toolbox

▪ cwt

▪ filter

▪ dwt/modwt

▪ pwelch

▪ periodogram

▪ xcov

▪ findpeaks

▪ …

42

Thank You

Signal Processing for Deep Learning and Machine Learning · Signal Processing for Deep Learning and...

Documents

Transcript of Signal Processing for Deep Learning and Machine Learning · Signal Processing for Deep Learning and...