DeepLearning.ai深度学习课程笔记
  • Introduction
  • 第一门课 神经网络和深度学习(Neural-Networks-and-Deep-Learning)
    • 第一周：深度学习引言(Introduction to Deep Learning)
      • 1.1 神经网络的监督学习(Supervised Learning with Neural Networks)
      • 1.2 为什么神经网络会流行？(Why is Deep Learning taking off?)
    • 第二周：神经网络的编程基础(Basics of Neural Network programming)
      • 2.1 二分类(Binary Classification)
      • 2.2 逻辑回归(Logistic Regression)
      • 2.3 逻辑回归的代价函数（Logistic Regression Cost Function）
      • 2.4 逻辑回归的梯度下降（Logistic Regression Gradient Descent）
      • 2.5 梯度下降的例子(Gradient Descent on m Examples)
      • 2.6 向量化 logistic 回归的梯度输出（Vectorizing Logistic Regression’s Gradient Output）
      • 2.7 （选修）logistic 损失函数的解释（Explanation of logistic regression cost function ）
      • Logistic Regression with a Neural Network mindset 代码
      • lr_utils.py
    • 第三周：浅层神经网络(Shallow neural networks)
      • 3.1 神经网络概述（Neural Network Overview）
      • 3.2 神经网络的表示（Neural Network Representation ）
      • 3.3 计算一个神经网络的输出（Computing a Neural Network's output ）
      • 3.4 多样本向量化（Vectorizing across multiple examples ）
      • 3.5 激活函数（Activation functions）
      • 3.6 为什么需要（ 非线性激活函数？（why need a nonlinear activation function?）
      • 3.7 激活函数的导数（Derivatives of activation functions ）
      • 3.8 神经网络的梯度下降（Gradient descent for neural networks）
      • 3.9 （选修）直观理解反向传播（Backpropagation intuition ）
      • 3.10 随机初始化（Random+Initialization）
      • Planar data classification with one hidden layer
      • planar_utils.py
      • testCases.py
    • 第四周：深层神经网络(Deep Neural Networks)
      • 4.1 深层神经网络（Deep L-layer neural network）
      • 4.2 前向传播和反向传播（Forward and backward propagation）
      • 4.3 深层网络中的前向传播（Forward propagation in a Deep Network ）
      • 4.4 为什么使用深层表示？（Why deep representations?）
      • 4.5 搭建神经网络块（Building blocks of deep neural networks）
      • 4.6 参数 VS 超参数（Parameters vs Hyperparameters）
      • Building your Deep Neural Network Step by Step
      • dnn_utils.py
      • testCases.py
      • Deep Neural Network Application
      • dnn_app_utils.py
  • 第二门课 改善深层神经网络：超参数调试、 正 则 化 以 及 优 化 (Improving Deep Neural Networks:Hyperparameter tuning, Regulariza
    • 第二门课 改善深层神经网络：超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization and
      • 第一周：深度学习的实用层面(Practical aspects of Deep Learning)
        • 1.1 训练，验证，测试集（Train / Dev / Test sets）
        • 1.2 偏差，方差（Bias /Variance）
        • 1.3 机器学习基础（Basic Recipe for Machine Learning）
        • 1.4 正则化（Regularization）
        • 1.5 为什么正则化有利于预防过拟合呢？（Why regularization reduces overfitting?）
        • 1.6 dropout 正则化（Dropout Regularization）
        • 1.7 理解 dropout（Understanding Dropout）
        • 1.8 其他正则化方法（Other regularization methods）
        • 1.9 归一化输入（Normalizing inputs）
        • 1.10 梯度消失/梯度爆炸（Vanishing / Exploding gradients）
        • 1.11 神经网络的权重初始化（Weight Initialization for Deep Networks）
        • 1.12 梯度的数值逼近（Numerical approximation of gradients）
        • 1.13 梯度检验（Gradient checking）
        • 1.14 梯度检验应用的注意事项（Gradient Checking Implementation Notes）
        • Initialization
        • Gradient Checking
        • Regularization
        • reg_utils.py
        • testCases.py
      • 第二周：优化算法 (Optimization algorithms)
        • 2.1 Mini-batch 梯度下降（Mini-batch gradient descent）
        • 2.2 理解 mini-batch 梯度下降法（Understanding mini-batch gradient descent）
        • 2.3 指数加权平均数（Exponentially weighted averages）
        • 2.4 理解指数加权平均数（Understanding exponentially weighted averages ）
        • 2.5 指 数 加 权 平 均 的 偏 差 修 正 （ Bias correction in exponentially weighted averages ）
        • 2.6 动量梯度下降法（Gradient descent with Momentum ）
        • 2.7 RMSprop( root mean square prop)
        • 2.8 Adam 优化算法(Adam optimization algorithm)
        • 2.9 学习率衰减(Learning rate decay)
        • 2.10 局部最优的问题(The problem of local optima)
        • Optimization
        • opt_utils.py
        • testCases.py
      • 第 三 周 超 参 数 调 试 、 Batch 正 则 化 和 程 序 框 架 （Hyperparameter tuning）
        • 3.1 调试处理（Tuning process）
        • 3.2 为超参数选择合适的范围（Using an appropriate scale to pick hyperparameters）
        • 3.3 超参数训练的实践： Pandas VS Caviar（Hyperparameters tuning in practice: Pandas vs. Caviar）
        • 3.4 归一化网络的激活函数（ Normalizing activations in a network）
        • 3.5 将 Batch Norm 拟合进神经网络（Fitting Batch Norm into a neural network）
        • 3.6 Batch Norm 为什么奏效？（Why does Batch Norm work?）
        • 3.7 测试时的 Batch Norm（Batch Norm at test time）
        • 3.8 Softmax 回归（Softmax regression）
        • 3.9 训练一个 Softmax 分类器（Training a Softmax classifier）
        • tensorflow tutorial
        • improv_utils.py
        • tf_utils.py
  • 第三门课 结构化机器学习项目（Structuring Machine Learning Projects）
    • 第三门课 结构化机器学习项目（Structuring Machine Learning Projects）
      • 第一周 机器学习（ML）策略（1）（ML strategy（1））
        • 1.1 为什么是 ML 策略？（Why ML Strategy?）
        • 1.2 正交化（Orthogonalization）
        • 1.3 单一数字评估指标（Single number evaluation metric）
        • 1.4 满足和优化指标（Satisficing and optimizing metrics）
        • 1.5 训练/开发/测试集划分（Train/dev/test distributions）
        • 1.6 开发集和测试集的大小（Size of dev and test sets）
        • 1.7 什么时候该改变开发/测试集和指标？（When to change dev/test sets and metrics）
        • 1.8 为什么是人的表现？（ Why human-level performance?）
        • 1.9 可避免偏差（Avoidable bias）
        • 1.10 理解人的表现（Understanding human-level performance）
        • 1.11 超过人的表现（Surpassing human- level performance）
        • 1.12 改善你的模型的表现（Improving your model performance）
      • 第二周：机器学习策略（2）(ML Strategy (2))
        • 2.1 进行误差分析（Carrying out error analysis）
        • 2.2 清楚标注错误的数据（Cleaning up Incorrectly labeled data）
        • 2.3 快速搭建你的第一个系统，并进行迭代（Build your first system quickly, then iterate）
        • 2.4 在不同的划分上进行训练并测试（Training and testing on different distributions）
        • 2.5 不匹配数据划分的偏差和方差（Bias and Variance with mismatched data distributions）
        • 2.6 定位数据不匹配（Addressing data mismatch）
        • 2.7 迁移学习（Transfer learning）
        • 2.8 多任务学习（Multi-task learning）
        • 2.9 什么是端到端的深度学习？（What is end-to-end deep learning?）
        • 2.10 是否要使用端到端的深度学习？（Whether to use end-to-end learning?）
  • 第四门课 卷积神经网络（Convolutional Neural Networks）
    • 第四门课 卷积神经网络（Convolutional Neural Networks）
      • 第一周 卷积神经网络（Foundations of Convolutional Neural Networks）
        • 1.1 计算机视觉（Computer vision）
        • 1.2 边缘检测示例（Edge detection example）
        • 1.3 更多边缘检测内容（More edge detection）
        • 1.4 Padding
        • 1.5 卷积步长（Strided convolutions）
        • 1.6 三维卷积（Convolutions over volumes）
        • 1.7 单层卷积网络（One layer of a convolutional network）
        • 1.8 简单卷积网络示例（A simple convolution network example）
        • 1.9 池化层（Pooling layers）
        • 1.10 卷积神经网络示例（Convolutional neural network example）
        • 1.11 为什么使用卷积？（Why convolutions?）
        • Convolution model Step by Step
        • Convolutional Neural Networks: Application
        • cnn_utils
      • 第二周 深度卷积网络：实例探究（Deep convolutional models: case studies）
        • 2.1 经典网络（Classic networks）
        • 2.2 残差网络（Residual Networks (ResNets)）
        • 2.3 残差网络为什么有用？（Why ResNets work?）
        • 2.4 网络中的网络以及 1×1 卷积（Network in Network and 1×1 convolutions）
        • 2.5 谷歌 Inception 网络简介（Inception network motivation）
        • 2.6 Inception 网络（Inception network）
        • 2.7 迁移学习（Transfer Learning）
        • 2.8 数据扩充（Data augmentation）
        • 2.9 计算机视觉现状（The state of computer vision）
        • Residual Networks
        • Keras tutorial - the Happy House
        • kt_utils.py
      • 第三周 目标检测（Object detection）
        • 3.1 目标定位（Object localization）
        • 3.2 特征点检测（Landmark detection）
        • 3.3 目标检测（Object detection）
        • 3.4 卷积的滑动窗口实现（Convolutional implementation of sliding windows）
        • 3.5 Bounding Box预测（Bounding box predictions）
        • 3.6 交并比（Intersection over union）
        • 3.7 非极大值抑制（Non-max suppression）
        • 3.8 Anchor Boxes
        • 3.9 YOLO 算法（Putting it together: YOLO algorithm）
        • 3.10 候选区域（选修）（Region proposals (Optional)）
        • Autonomous driving application - Car detection
        • yolo_utils.py
      • 第四周 特殊应用：人脸识别和神经风格转换（Special applications: Face recognition &Neural style transfer）
        • 4.1 什么是人脸识别？（What is face recognition?）
        • 4.2 One-Shot学习（One-shot learning）
        • 4.3 Siamese 网络（Siamese network）
        • 4.4 Triplet 损失（Triplet 损失）
        • 4.5 面部验证与二分类（Face verification and binary classification）
        • 4.6 什么是深度卷积网络？（What are deep ConvNets learning?）
        • 4.7 代价函数（Cost function）
        • 4.8 内容代价函数（Content cost function）
        • 4.9 风格代价函数（Style cost function）
        • 4.10 一维到三维推广（1D and 3D generalizations of models）
        • Art Generation with Neural Style Transfer
        • nst_utils.py
        • Face Recognition for the Happy House
        • fr_utils.py
        • inception_blocks.py
  • 第五门课 序列模型(Sequence Models)
    • 第五门课 序列模型(Sequence Models)
      • 第一周 循环序列模型（Recurrent Neural Networks）
        • 1.1 为什么选择序列模型？（Why Sequence Models?）
        • 1.2 数学符号（Notation）
        • 1.3 循环神经网络模型（Recurrent Neural Network Model）
        • 1.4 通过时间的反向传播（Backpropagation through time）
        • 1.5 不同类型的循环神经网络（Different types of RNNs）
        • 1.6 语言模型和序列生成（Language model and sequence generation）
        • 1.7 对新序列采样（Sampling novel sequences）
        • 1.8 循环神经网络的梯度消失（Vanishing gradients with RNNs）
        • 1.9 GRU单元（Gated Recurrent Unit（GRU））
        • 1.10 长短期记忆（LSTM（long short term memory）unit）
        • 1.11 双向循环神经网络（Bidirectional RNN）
        • 1.12 深层循环神经网络（Deep RNNs）
        • Building your Recurrent Neural Network
        • rnn_utils.py
        • Dinosaurus Island -- Character level language model final
        • utils.py
        • shakespeare_utils.py
        • Improvise a Jazz Solo with an LSTM Network
      • 第二周 自然语言处理与词嵌入（Natural Language Processing and Word Embeddings）
        • 2.1 词汇表征（Word Representation）
        • 2.2 使用词嵌入（Using Word Embeddings）
        • 2.3 词嵌入的特性（Properties of Word Embeddings）
        • 2.4 嵌入矩阵（Embedding Matrix）
        • 2.5 学习词嵌入（Learning Word Embeddings）
        • 2.6 Word2Vec
        • 2.7 负采样（Negative Sampling）
        • 2.8 GloVe 词向量（GloVe Word Vectors）
        • 2.9 情感分类（Sentiment Classification）
        • 2.10 词嵌入除偏（Debiasing Word Embeddings）
        • Operations on word vectors
        • w2v_utils.py
        • Emojify
        • emo_utils.py
      • 第三周 序列模型和注意力机制（Sequence models & Attention mechanism）
        • 3.1 基础模型（Basic Models）
        • 3.2 选择最可能的句子（Picking the most likely sentence）
        • 3.3 集束搜索（Beam Search）
        • 3.4 改进集束搜索（Refinements to Beam Search）
        • 3.5 集束搜索的误差分析（Error analysis in beam search）
        • 3.6 Bleu 得分（选修）（Bleu Score (optional)）
        • 3.7 注意力模型直观理解（Attention Model Intuition）
        • 3.8注意力模型（Attention Model）
        • 3.9语音识别（Speech recognition）
        • 3.10触发字检测（Trigger Word Detection）
        • Neural machine translation with attention
        • nmt_utils.py
        • Trigger word detection
        • td_utils.py
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On this page
  • Trigger Word Detection
  • 1 - Data synthesis: Creating a speech dataset
  • 1.1 - Listening to the data
  • 1.2 - From audio recordings to spectrograms
  • 1.3 - Generating a single training example
  • 1.4 - Full training set
  • 1.5 - Development set
  • 2 - Model
  • 2.1 - Build the model
  • 2.2 - Fit the model
  • 2.3 - Test the model
  • 3 - Making Predictions
  • 3.3 - Test on dev examples
  • Congratulations
  • 4 - Try your own example! (OPTIONAL/UNGRADED)

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  1. 第五门课 序列模型(Sequence Models)
  2. 第五门课 序列模型(Sequence Models)
  3. 第三周 序列模型和注意力机制（Sequence models & Attention mechanism）

Trigger word detection

Previousnmt_utils.pyNexttd_utils.py

Last updated 6 years ago

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Trigger Word Detection

Welcome to the final programming assignment of this specialization!

In this week's videos, you learned about applying deep learning to speech recognition. In this assignment, you will construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word.

For this exercise, our trigger word will be "Activate." Every time it hears you say "activate," it will make a "chiming" sound. By the end of this assignment, you will be able to record a clip of yourself talking, and have the algorithm trigger a chime when it detects you saying "activate."

After completing this assignment, perhaps you can also extend it to run on your laptop so that every time you say "activate" it starts up your favorite app, or turns on a network connected lamp in your house, or triggers some other event?

In this assignment you will learn to:

  • Structure a speech recognition project

  • Synthesize and process audio recordings to create train/dev datasets

  • Train a trigger word detection model and make predictions

Lets get started! Run the following cell to load the package you are going to use.

import numpy as np
from pydub import AudioSegment
import random
import sys
import io
import os
import glob
import Ipy
from td_utils import *
%matplotlib inline

1 - Data synthesis: Creating a speech dataset

Let's start by building a dataset for your trigger word detection algorithm. A speech dataset should ideally be as close as possible to the application you will want to run it on. In this case, you'd like to detect the word "activate" in working environments (library, home, offices, open-spaces ...). You thus need to create recordings with a mix of positive words ("activate") and negative words (random words other than activate) on different background sounds. Let's see how you can create such a dataset.

1.1 - Listening to the data

One of your friends is helping you out on this project, and they've gone to libraries, cafes, restaurants, homes and offices all around the region to record background noises, as well as snippets of audio of people saying positive/negative words. This dataset includes people speaking in a variety of accents.

In the raw_data directory, you can find a subset of the raw audio files of the positive words, negative words, and background noise. You will use these audio files to synthesize a dataset to train the model. The "activate" directory contains positive examples of people saying the word "activate". The "negatives" directory contains negative examples of people saying random words other than "activate". There is one word per audio recording. The "backgrounds" directory contains 10 second clips of background noise in different environments.

Run the cells below to listen to some examples.

Ipy.display.Audio("./raw_data/activates/1.wav")
Ipy.display.Audio("./raw_data/negatives/4.wav")
Ipy.display.Audio("./raw_data/backgrounds/1.wav")

You will use these three type of recordings (positives/negatives/backgrounds) to create a labelled dataset.

1.2 - From audio recordings to spectrograms

It is quite difficult to figure out from this "raw" representation of audio whether the word "activate" was said. In order to help your sequence model more easily learn to detect triggerwords, we will compute a spectrogram of the audio. The spectrogram tells us how much different frequencies are present in an audio clip at a moment in time.

(If you've ever taken an advanced class on signal processing or on Fourier transforms, a spectrogram is computed by sliding a window over the raw audio signal, and calculates the most active frequencies in each window using a Fourier transform. If you don't understand the previous sentence, don't worry about it.)

Lets see an example.

Ipy.display.Audio("audio_examples/example_train.wav")
x = graph_spectrogram("audio_examples/example_train.wav")

Figure 1: Spectrogram of an audio recording, where the color shows the degree to which different frequencies are present (loud) in the audio at different points in time. Green squares means a certain frequency is more active or more present in the audio clip (louder); blue squares denote less active frequencies.

_, data = wavfile.read("audio_examples/example_train.wav")
print("Time steps in audio recording before spectrogram", data[:,0].shape)
print("Time steps in input after spectrogram", x.shape)
Time steps in audio recording before spectrogram (441000,)
Time steps in input after spectrogram (101, 5511)

Now, you can define:

Tx = 5511 # The number of time steps input to the model from the spectrogram
n_freq = 101 # Number of frequencies input to the model at each time step of the spectrogram

For the 10sec of audio, the key values you will see in this assignment are:

Note that each of these representations correspond to exactly 10 seconds of time. It's just that they are discretizing them to different degrees. All of these are hyperparameters and can be changed (except the 441000, which is a function of the microphone). We have chosen values that are within the standard ranges uses for speech systems.

Consider also the 10000 number above. This corresponds to discretizing the 10sec clip into 10/10000 = 0.001 second itervals. 0.001 seconds is also called 1 millisecond, or 1ms. So when we say we are discretizing according to 1ms intervals, it means we are using 10,000 steps.

Ty = 1375 # The number of time steps in the output of our model

1.3 - Generating a single training example

Because speech data is hard to acquire and label, you will synthesize your training data using the audio clips of activates, negatives, and backgrounds. It is quite slow to record lots of 10 second audio clips with random "activates" in it. Instead, it is easier to record lots of positives and negative words, and record background noise separately (or download background noise from free online sources).

To synthesize a single training example, you will:

  • Pick a random 10 second background audio clip

  • Randomly insert 0-4 audio clips of "activate" into this 10sec clip

  • Randomly insert 0-2 audio clips of negative words into this 10sec clip

You will use the pydub package to manipulate audio. Pydub converts raw audio files into lists of Pydub data structures (it is not important to know the details here). Pydub uses 1ms as the discretization interval (1ms is 1 millisecond = 1/1000 seconds) which is why a 10sec clip is always represented using 10,000 steps.

# Load audio segments using pydub 
activates, negatives, backgrounds = load_raw_audio()

print("background len: " + str(len(backgrounds[0])))    # Should be 10,000, since it is a 10 sec clip
print("activate[0] len: " + str(len(activates[0])))     # Maybe around 1000, since an "activate" audio clip is usually around 1 sec (but varies a lot)
print("activate[1] len: " + str(len(activates[1])))     # Different "activate" clips can have different lengths
background len: 10000
activate[0] len: 916
activate[1] len: 1579

Overlaying positive/negative words on the background:

Given a 10sec background clip and a short audio clip (positive or negative word), you need to be able to "add" or "insert" the word's short audio clip onto the background. To ensure audio segments inserted onto the background do not overlap, you will keep track of the times of previously inserted audio clips. You will be inserting multiple clips of positive/negative words onto the background, and you don't want to insert an "activate" or a random word somewhere that overlaps with another clip you had previously added.

For clarity, when you insert a 1sec "activate" onto a 10sec clip of cafe noise, you end up with a 10sec clip that sounds like someone sayng "activate" in a cafe, with "activate" superimposed on the background cafe noise. You do not end up with an 11 sec clip. You'll see later how pydub allows you to do this.

Creating the labels at the same time you overlay:

Figure 2

To implement the training set synthesis process, you will use the following helper functions. All of these function will use a 1ms discretization interval, so the 10sec of audio is alwsys discretized into 10,000 steps.

  1. get_random_time_segment(segment_ms) gets a random time segment in our background audio

  2. is_overlapping(segment_time, existing_segments) checks if a time segment overlaps with existing segments

  3. insert_audio_clip(background, audio_clip, existing_times) inserts an audio segment at a random time in our background audio using get_random_time_segment and is_overlapping

  4. insert_ones(y, segment_end_ms) inserts 1's into our label vector y after the word "activate"

The function get_random_time_segment(segment_ms) returns a random time segment onto which we can insert an audio clip of duration segment_ms. Read through the code to make sure you understand what it is doing.

def get_random_time_segment(segment_ms):
    """
    Gets a random time segment of duration segment_ms in a 10,000 ms audio clip.

    Arguments:
    segment_ms -- the duration of the audio clip in ms ("ms" stands for "milliseconds")

    Returns:
    segment_time -- a tuple of (segment_start, segment_end) in ms
    """

    segment_start = np.random.randint(low=0, high=10000-segment_ms)   # Make sure segment doesn't run past the 10sec background 
    segment_end = segment_start + segment_ms - 1

    return (segment_start, segment_end)

Next, suppose you have inserted audio clips at segments (1000,1800) and (3400,4500). I.e., the first segment starts at step 1000, and ends at step 1800. Now, if we are considering inserting a new audio clip at (3000,3600) does this overlap with one of the previously inserted segments? In this case, (3000,3600) and (3400,4500) overlap, so we should decide against inserting a clip here.

For the purpose of this function, define (100,200) and (200,250) to be overlapping, since they overlap at timestep 200. However, (100,199) and (200,250) are non-overlapping.

Exercise: Implement is_overlapping(segment_time, existing_segments) to check if a new time segment overlaps with any of the previous segments. You will need to carry out 2 steps:

  1. Create a "False" flag, that you will later set to "True" if you find that there is an overlap.

  2. Loop over the previous_segments' start and end times. Compare these times to the segment's start and end times. If there is an overlap, set the flag defined in (1) as True. You can use:

    for ....:
     if ... <= ... and ... >= ...:
         ...

    Hint: There is overlap if the segment starts before the previous segment ends, and the segment ends after the previous segment starts.

# GRADED FUNCTION: is_overlapping

def is_overlapping(segment_time, previous_segments):
    """
    Checks if the time of a segment overlaps with the times of existing segments.

    Arguments:
    segment_time -- a tuple of (segment_start, segment_end) for the new segment
    previous_segments -- a list of tuples of (segment_start, segment_end) for the existing segments

    Returns:
    True if the time segment overlaps with any of the existing segments, False otherwise
    """

    segment_start, segment_end = segment_time

    ### START CODE HERE ### (≈ 4 line)
    # Step 1: Initialize overlap as a "False" flag. (≈ 1 line)
    overlap = False

    # Step 2: loop over the previous_segments start and end times.
    # Compare start/end times and set the flag to True if there is an overlap (≈ 3 lines)
    for previous_start, previous_end in previous_segments:
        if segment_start <= previous_end and segment_end >= previous_start:
            overlap = True
    ### END CODE HERE ###

    return overlap
overlap1 = is_overlapping((950, 1430), [(2000, 2550), (260, 949)])
overlap2 = is_overlapping((2305, 2950), [(824, 1532), (1900, 2305), (3424, 3656)])
print("Overlap 1 = ", overlap1)
print("Overlap 2 = ", overlap2)
Overlap 1 =  False
Overlap 2 =  True

Now, lets use the previous helper functions to insert a new audio clip onto the 10sec background at a random time, but making sure that any newly inserted segment doesn't overlap with the previous segments.

Exercise: Implement insert_audio_clip() to overlay an audio clip onto the background 10sec clip. You will need to carry out 4 steps:

  1. Get a random time segment of the right duration in ms.

  2. Make sure that the time segment does not overlap with any of the previous time segments. If it is overlapping, then go back to step 1 and pick a new time segment.

  3. Add the new time segment to the list of existing time segments, so as to keep track of all the segments you've inserted.

  4. Overlay the audio clip over the background using pydub. We have implemented this for you.

# GRADED FUNCTION: insert_audio_clip

def insert_audio_clip(background, audio_clip, previous_segments):
    """
    Insert a new audio segment over the background noise at a random time step, ensuring that the 
    audio segment does not overlap with existing segments.

    Arguments:
    background -- a 10 second background audio recording.  
    audio_clip -- the audio clip to be inserted/overlaid. 
    previous_segments -- times where audio segments have already been placed

    Returns:
    new_background -- the updated background audio
    """

    # Get the duration of the audio clip in ms
    segment_ms = len(audio_clip)

    ### START CODE HERE ### 
    # Step 1: Use one of the helper functions to pick a random time segment onto which to insert 
    # the new audio clip. (≈ 1 line)
    segment_time = get_random_time_segment(segment_ms)

    # Step 2: Check if the new segment_time overlaps with one of the previous_segments. If so, keep 
    # picking new segment_time at random until it doesn't overlap. (≈ 2 lines)
    while is_overlapping(segment_time,previous_segments):
        segment_time = get_random_time_segment(segment_ms)

    # Step 3: Add the new segment_time to the list of previous_segments (≈ 1 line)
    previous_segments.append(segment_time)
    ### END CODE HERE ###

    # Step 4: Superpose audio segment and background
    new_background = background.overlay(audio_clip, position = segment_time[0])

    return new_background, segment_time
np.random.seed(5)
audio_clip, segment_time = insert_audio_clip(backgrounds[0], activates[0], [(3790, 4400)])
audio_clip.export("insert_test.wav", format="wav")
print("Segment Time: ", segment_time)
Ipy.display.Audio("insert_test.wav")
Segment Time:  (2254, 3169)
# Expected audio
Ipy.display.Audio("audio_examples/insert_reference.wav")
segment_end_y = int(segment_end_ms * Ty / 10000.0)
# GRADED FUNCTION: insert_ones

def insert_ones(y, segment_end_ms):
    """
    Update the label vector y. The labels of the 50 output steps strictly after the end of the segment 
    should be set to 1. By strictly we mean that the label of segment_end_y should be 0 while, the
    50 followinf labels should be ones.


    Arguments:
    y -- numpy array of shape (1, Ty), the labels of the training example
    segment_end_ms -- the end time of the segment in ms

    Returns:
    y -- updated labels
    """

    # duration of the background (in terms of spectrogram time-steps)
    segment_end_y = int(segment_end_ms * Ty / 10000.0)

    # Add 1 to the correct index in the background label (y)
    ### START CODE HERE ### (≈ 3 lines)
    for i in range(segment_end_y + 1, segment_end_y + 51):
        if i < Ty:
            y[0, i] = 1
    ### END CODE HERE ###

    return y
arr1 = insert_ones(np.zeros((1, Ty)), 9700)
plt.plot(insert_ones(arr1, 4251)[0,:])
print("sanity checks:", arr1[0][1333], arr1[0][634], arr1[0][635])
sanity checks: 0.0 1.0 0.0

Finally, you can use insert_audio_clip and insert_ones to create a new training example.

Exercise: Implement create_training_example(). You will need to carry out the following steps:

  2. Initialize the set of existing segments to an empty list.

  4. Randomly select 0 to 2 negative audio clips, and insert them into the 10sec clip. 

# GRADED FUNCTION: create_training_example

def create_training_example(background, activates, negatives):
    """
    Creates a training example with a given background, activates, and negatives.

    Arguments:
    background -- a 10 second background audio recording
    activates -- a list of audio segments of the word "activate"
    negatives -- a list of audio segments of random words that are not "activate"

    Returns:
    x -- the spectrogram of the training example
    y -- the label at each time step of the spectrogram
    """

    # Set the random seed
    np.random.seed(18)

    # Make background quieter
    background = background - 20

    ### START CODE HERE ###
    # Step 1: Initialize y (label vector) of zeros (≈ 1 line)
    y = np.zeros((1, Ty))

    # Step 2: Initialize segment times as empty list (≈ 1 line)
    previous_segments = []
    ### END CODE HERE ###

    # Select 0-4 random "activate" audio clips from the entire list of "activates" recordings
    number_of_activates = np.random.randint(0, 5)
    random_indices = np.random.randint(len(activates), size=number_of_activates)
    random_activates = [activates[i] for i in random_indices]

    ### START CODE HERE ### (≈ 3 lines)
    # Step 3: Loop over randomly selected "activate" clips and insert in background
    for random_activate in random_activates:
        # Insert the audio clip on the background
        background, segment_time = insert_audio_clip(background, random_activate, previous_segments)
        # Retrieve segment_start and segment_end from segment_time
        segment_start, segment_end = segment_time
        # Insert labels in "y"
        y = insert_ones(y, segment_end)
    ### END CODE HERE ###

    # Select 0-2 random negatives audio recordings from the entire list of "negatives" recordings
    number_of_negatives = np.random.randint(0, 3)
    random_indices = np.random.randint(len(negatives), size=number_of_negatives)
    random_negatives = [negatives[i] for i in random_indices]

    ### START CODE HERE ### (≈ 2 lines)
    # Step 4: Loop over randomly selected negative clips and insert in background
    for random_negative in random_negatives:
        # Insert the audio clip on the background 
        background, _ = insert_audio_clip(background, random_negative, previous_segments)
    ### END CODE HERE ###

    # Standardize the volume of the audio clip 
    background = match_target_amplitude(background, -20.0)

    # Export new training example 
    file_handle = background.export("train" + ".wav", format="wav")
    print("File (train.wav) was saved in your directory.")

    # Get and plot spectrogram of the new recording (background with superposition of positive and negatives)
    x = graph_spectrogram("train.wav")

    return x, y
x, y = create_training_example(backgrounds[0], activates, negatives)
File (train.wav) was saved in your directory.

Now you can listen to the training example you created and compare it to the spectrogram generated above.

Ipy.display.Audio("train.wav")

Expected Output

Ipy.display.Audio("audio_examples/train_reference.wav")

Finally, you can plot the associated labels for the generated training example.

plt.plot(y[0])

[<matplotlib.lines.Line2D at 0x7f32748e8550>]

Expected Output

1.4 - Full training set

You've now implemented the code needed to generate a single training example. We used this process to generate a large training set. To save time, we've already generated a set of training examples.

# Load preprocessed training examples
X = np.load("./XY_train/X.npy")
Y = np.load("./XY_train/Y.npy")

1.5 - Development set

To test our model, we recorded a development set of 25 examples. While our training data is synthesized, we want to create a development set using the same distribution as the real inputs. Thus, we recorded 25 10-second audio clips of people saying "activate" and other random words, and labeled them by hand. This follows the principle described in Course 3 that we should create the dev set to be as similar as possible to the test set distribution; that's why our dev set uses real rather than synthesized audio.

# Load preprocessed dev set examples
X_dev = np.load("./XY_dev/X_dev.npy")
Y_dev = np.load("./XY_dev/Y_dev.npy")

2 - Model

Now that you've built a dataset, lets write and train a trigger word detection model!

The model will use 1-D convolutional layers, GRU layers, and dense layers. Let's load the packages that will allow you to use these layers in Keras. This might take a minute to load.

from keras.callbacks import ModelCheckpoint
from keras.models import Model, load_model, Sequential
from keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D
from keras.layers import GRU, Bidirectional, BatchNormalization, Reshape
from keras.optimizers import Adam
Using TensorFlow backend.

2.1 - Build the model

Figure 3

Note that we use a uni-directional RNN rather than a bi-directional RNN. This is really important for trigger word detection, since we want to be able to detect the trigger word almost immediately after it is said. If we used a bi-directional RNN, we would have to wait for the whole 10sec of audio to be recorded before we could tell if "activate" was said in the first second of the audio clip.

Implementing the model can be done in four steps:

Step 2: First GRU layer. To generate the GRU layer, use:

X = GRU(units = 128, return_sequences = True)(X)

Setting return_sequences=True ensures that all the GRU's hidden states are fed to the next layer. Remember to follow this with Dropout and BatchNorm layers.

Step 3: Second GRU layer. This is similar to the previous GRU layer (remember to use return_sequences=True), but has an extra dropout layer.

Step 4: Create a time-distributed dense layer as follows:

X = TimeDistributed(Dense(1, activation = "sigmoid"))(X)

Exercise: Implement model(), the architecture is presented in Figure 3.

# GRADED FUNCTION: model

def model(input_shape):
    """
    Function creating the model's graph in Keras.

    Argument:
    input_shape -- shape of the model's input data (using Keras conventions)

    Returns:
    model -- Keras model instance
    """

    X_input = Input(shape = input_shape)

    ### START CODE HERE ###

    # Step 1: CONV layer (≈4 lines)
    X = Conv1D(filters = 196, kernel_size = 15, strides = 4)(X_input)                                 # CONV1D
    X = BatchNormalization()(X)                                 # Batch normalization
    X = Activation('relu')(X)                                 # ReLu activation
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)

    # Step 2: First GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences = True)(X)                                 # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)
    X = BatchNormalization()(X)                                 # Batch normalization

    # Step 3: Second GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences = True)(X)                                 # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)
    X = BatchNormalization()(X)                                 # Batch normalization
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)

    # Step 4: Time-distributed dense layer (≈1 line)
    X = TimeDistributed(Dense(1, activation = "sigmoid"))(X) # time distributed  (sigmoid)

    ### END CODE HERE ###

    model = Model(inputs = X_input, outputs = X)

    return model
model = model(input_shape = (Tx, n_freq))

Let's print the model summary to keep track of the shapes.

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 5511, 101)         0         
_________________________________________________________________
conv1d_1 (Conv1D)            (None, 1375, 196)         297136    
_________________________________________________________________
batch_normalization_1 (Batch (None, 1375, 196)         784       
_________________________________________________________________
activation_1 (Activation)    (None, 1375, 196)         0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 1375, 196)         0         
_________________________________________________________________
gru_1 (GRU)                  (None, 1375, 128)         124800    
_________________________________________________________________
dropout_2 (Dropout)          (None, 1375, 128)         0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 1375, 128)         512       
_________________________________________________________________
gru_2 (GRU)                  (None, 1375, 128)         98688     
_________________________________________________________________
dropout_3 (Dropout)          (None, 1375, 128)         0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 1375, 128)         512       
_________________________________________________________________
dropout_4 (Dropout)          (None, 1375, 128)         0         
_________________________________________________________________
time_distributed_1 (TimeDist (None, 1375, 1)           129       
=================================================================
Total params: 522,561
Trainable params: 521,657
Non-trainable params: 904
_________________________________________________________________

The output of the network is of shape (None, 1375, 1) while the input is (None, 5511, 101). The Conv1D has reduced the number of steps from 5511 at spectrogram to 1375.

2.2 - Fit the model

Trigger word detection takes a long time to train. To save time, we've already trained a model for about 3 hours on a GPU using the architecture you built above, and a large training set of about 4000 examples. Let's load the model.

model = load_model('./models/tr_model.h5')

You can train the model further, using the Adam optimizer and binary cross entropy loss, as follows. This will run quickly because we are training just for one epoch and with a small training set of 26 examples.

opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=["accuracy"])
model.fit(X, Y, batch_size = 5, epochs=1)


    Epoch 1/1
    26/26 [==============================] - 25s - loss: 0.0727 - acc: 0.9806    





    <keras.callbacks.History at 0x7f321fdca898>

2.3 - Test the model

Finally, let's see how your model performs on the dev set.

loss, acc = model.evaluate(X_dev, Y_dev)
print("Dev set accuracy = ", acc)
25/25 [==============================] - 4s
Dev set accuracy =  0.946036338806

This looks pretty good! However, accuracy isn't a great metric for this task, since the labels are heavily skewed to 0's, so a neural network that just outputs 0's would get slightly over 90% accuracy. We could define more useful metrics such as F1 score or Precision/Recall. But let's not bother with that here, and instead just empirically see how the model does.

3 - Making Predictions

Now that you have built a working model for trigger word detection, let's use it to make predictions. This code snippet runs audio (saved in a wav file) through the network.

can use your model to make predictions on new audio clips.

You will first need to compute the predictions for an input audio clip.

Exercise: Implement predict_activates(). You will need to do the following:

  1. Compute the spectrogram for the audio file

  2. Use np.swap and np.expand_dims to reshape your input to size (1, Tx, n_freqs)

  3. Use forward propagation on your model to compute the prediction at each output step

def detect_triggerword(filename):
    plt.subplot(2, 1, 1)

    x = graph_spectrogram(filename)
    # the spectogram outputs (freqs, Tx) and we want (Tx, freqs) to input into the model
    x  = x.swapaxes(0,1)
    x = np.expand_dims(x, axis=0)
    predictions = model.predict(x)

    plt.subplot(2, 1, 2)
    plt.plot(predictions[0,:,0])
    plt.ylabel('probability')
    plt.show()
    return predictions

Exercise: Implement chime_on_activate(). You will need to do the following:

  1. Loop over the predicted probabilities at each output step

  2. When the prediction is larger than the threshold and more than 75 consecutive time steps have passed, insert a "chime" sound onto the original audio clip

Use this code to convert from the 1,375 step discretization to the 10,000 step discretization and insert a "chime" using pydub:

audio_clip = audio_clip.overlay(chime, position = ((i / Ty) * audio.duration_seconds)*1000)

chime_file = "audio_examples/chime.wav"
def chime_on_activate(filename, predictions, threshold):
    audio_clip = AudioSegment.from_wav(filename)
    chime = AudioSegment.from_wav(chime_file)
    Ty = predictions.shape[1]
    # Step 1: Initialize the number of consecutive output steps to 0
    consecutive_timesteps = 0
    # Step 2: Loop over the output steps in the y
    for i in range(Ty):
        # Step 3: Increment consecutive output steps
        consecutive_timesteps += 1
        # Step 4: If prediction is higher than the threshold and more than 75 consecutive output steps have passed
        if predictions[0,i,0] > threshold and consecutive_timesteps > 75:
            # Step 5: Superpose audio and background using pydub
            audio_clip = audio_clip.overlay(chime, position = ((i / Ty) * audio_clip.duration_seconds)*1000)
            # Step 6: Reset consecutive output steps to 0
            consecutive_timesteps = 0

    audio_clip.export("chime_output.wav", format='wav')

3.3 - Test on dev examples

Let's explore how our model performs on two unseen audio clips from the development set. Lets first listen to the two dev set clips.

Ipy.display.Audio("./raw_data/dev/1.wav")
Ipy.display.Audio("./raw_data/dev/2.wav")

Now lets run the model on these audio clips and see if it adds a chime after "activate"!

filename = "./raw_data/dev/1.wav"
prediction = detect_triggerword(filename)
chime_on_activate(filename, prediction, 0.5)
Ipy.display.Audio("./chime_output.wav")
filename  = "./raw_data/dev/2.wav"
prediction = detect_triggerword(filename)
chime_on_activate(filename, prediction, 0.5)
Ipy.display.Audio("./chime_output.wav")

Congratulations

You've come to the end of this assignment!

Here's what you should remember:

  • Data synthesis is an effective way to create a large training set for speech problems, specifically trigger word detection.

  • Using a spectrogram and optionally a 1D conv layer is a common pre-processing step prior to passing audio data to an RNN, GRU or LSTM.

  • An end-to-end deep learning approach can be used to built a very effective trigger word detection system.

Congratulations on finishing the fimal assignment!

Thank you for sticking with us through the end and for all the hard work you've put into learning deep learning. We hope you have enjoyed the course!

4 - Try your own example! (OPTIONAL/UNGRADED)

In this optional and ungraded portion of this notebook, you can try your model on your own audio clips!

Record a 10 second audio clip of you saying the word "activate" and other random words, and upload it to the Coursera hub as myaudio.wav. Be sure to upload the audio as a wav file. If your audio is recorded in a different format (such as mp3) there is free software that you can find online for converting it to wav. If your audio recording is not 10 seconds, the code below will either trim or pad it as needed to make it 10 seconds.

# Preprocess the audio to the correct format
def preprocess_audio(filename):
    # Trim or pad audio segment to 10000ms
    padding = AudioSegment.silent(duration=10000)
    segment = AudioSegment.from_wav(filename)[:10000]
    segment = padding.overlay(segment)
    # Set frame rate to 44100
    segment = segment.set_frame_rate(44100)
    # Export as wav
    segment.export(filename, format='wav')

Once you've uploaded your audio file to Coursera, put the path to your file in the variable below.

your_filename = "audio_examples/my_audio.wav"
preprocess_audio(your_filename)
Ipy.display.Audio(your_filename) # listen to the audio you uploaded

Finally, use the model to predict when you say activate in the 10 second audio clip, and trigger a chime. If beeps are not being added appropriately, try to adjust the chime_threshold.

chime_threshold = 0.5
prediction = detect_triggerword(your_filename)
chime_on_activate(your_filename, prediction, chime_threshold)
Ipy.display.Audio("./chime_output.wav")

What really is an audio recording? A microphone records little variations in air pressure over time, and it is these little variations in air pressure that your ear also perceives as sound. You can think of an audio recording is a long list of numbers measuring the little air pressure changes detected by the microphone. We will use audio sampled at 44100 Hz (or 44100 Hertz). This means the microphone gives us 44100 numbers per second. Thus, a 10 second audio clip is represented by 441000 numbers (= 10×4410010 \times 4410010×44100).

The graph above represents how active each frequency is (y axis) over a number of time-steps (x axis).

The dimension of the output spectrogram depends upon the hyperparameters of the spectrogram software and the length of the input. In this notebook, we will be working with 10 second audio clips as the "standard length" for our training examples. The number of timesteps of the spectrogram will be 5511. You'll see later that the spectrogram will be the input xxx into the network, and so Tx=5511T_x = 5511Tx​=5511.

Note that even with 10 seconds being our default training example length, 10 seconds of time can be discretized to different numbers of value. You've seen 441000 (raw audio) and 5511 (spectrogram). In the former case, each step represents 10/441000≈0.00002310/441000 \approx 0.00002310/441000≈0.000023 seconds. In the second case, each step represents 10/5511≈0.001810/5511 \approx 0.001810/5511≈0.0018 seconds.

441000441000441000 (raw audio)

5511=Tx5511 = T_x5511=Tx​ (spectrogram output, and dimension of input to the neural network).

100001000010000 (used by the pydub module to synthesize audio)

1375=Ty1375 = T_y1375=Ty​ (the number of steps in the output of the GRU you'll build).

Consider the Ty=1375T_y = 1375Ty​=1375 number above. This means that for the output of the model, we discretize the 10s into 1375 time-intervals (each one of length 10/1375≈0.007210/1375 \approx 0.007210/1375≈0.0072s) and try to predict for each of these intervals whether someone recently finished saying "activate."

Because you had synthesized the word "activate" into the background clip, you know exactly when in the 10sec clip the "activate" makes its appearance. You'll see later that this makes it easier to generate the labels y⟨t⟩y^{\langle t \rangle}y⟨t⟩ as well.

Recall also that the labels y⟨t⟩y^{\langle t \rangle}y⟨t⟩ represent whether or not someone has just finished saying "activate." Given a background clip, we can initialize y⟨t⟩=0y^{\langle t \rangle}=0y⟨t⟩=0 for all ttt, since the clip doesn't contain any "activates."

When you insert or overlay an "activate" clip, you will also update labels for y⟨t⟩y^{\langle t \rangle}y⟨t⟩, so that 50 steps of the output now have target label 1. You will train a GRU to detect when someone has finished saying "activate". For example, suppose the synthesized "activate" clip ends at the 5sec mark in the 10sec audio---exactly halfway into the clip. Recall that Ty=1375T_y = 1375Ty​=1375, so timestep 687=687 =687= int(1375*0.5) corresponds to the moment at 5sec into the audio. So, you will set y⟨688⟩=1y^{\langle 688 \rangle} = 1y⟨688⟩=1. Further, you would quite satisfied if the GRU detects "activate" anywhere within a short time-internal after this moment, so we actually set 50 consecutive values of the label y⟨t⟩y^{\langle t \rangle}y⟨t⟩ to 1. Specifically, we have y⟨688⟩=y⟨689⟩=⋯=y⟨737⟩=1y^{\langle 688 \rangle} = y^{\langle 689 \rangle} = \cdots = y^{\langle 737 \rangle} = 1y⟨688⟩=y⟨689⟩=⋯=y⟨737⟩=1.

This is another reason for synthesizing the training data: It's relatively straightforward to generate these labels y⟨t⟩y^{\langle t \rangle}y⟨t⟩ as described above. In contrast, if you have 10sec of audio recorded on a microphone, it's quite time consuming for a person to listen to it and mark manually exactly when "activate" finished.

Here's a figure illustrating the labels y⟨t⟩y^{\langle t \rangle}y⟨t⟩, for a clip which we have inserted "activate", "innocent", activate", "baby." Note that the positive labels "1" are associated only with the positive words.

Finally, implement code to update the labels y⟨t⟩y^{\langle t \rangle}y⟨t⟩, assuming you just inserted an "activate." In the code below, y is a (1,1375) dimensional vector, since Ty=1375T_y = 1375Ty​=1375.

If the "activate" ended at time step ttt, then set y⟨t+1⟩=1y^{\langle t+1 \rangle} = 1y⟨t+1⟩=1 as well as for up to 49 additional consecutive values. However, make sure you don't run off the end of the array and try to update y[0][1375], since the valid indices are y[0][0] through y[0][1374] because Ty=1375T_y = 1375Ty​=1375. So if "activate" ends at step 1370, you would get only y[0][1371] = y[0][1372] = y[0][1373] = y[0][1374] = 1

Exercise: Implement insert_ones(). You can use a for loop. (If you are an expert in py's slice operations, feel free also to use slicing to vectorize this.) If a segment ends at segment_end_ms (using a 10000 step discretization), to convert it to the indexing for the outputs yyy (using a 137513751375 step discretization), we will use this formula:

Initialize the label vector yyy as a numpy array of zeros and shape (1,Ty)(1, T_y)(1,Ty​).

Randomly select 0 to 4 "activate" audio clips, and insert them onto the 10sec clip. Also insert labels at the correct position in the label vector yyy.

Expected Output

Here is the architecture we will use. Take some time to look over the model and see if it makes sense.

One key step of this model is the 1D convolutional step (near the bottom of Figure 3). It inputs the 5511 step spectrogram, and outputs a 1375 step output, which is then further processed by multiple layers to get the final Ty=1375T_y = 1375Ty​=1375 step output. This layer plays a role similar to the 2D convolutions you saw in Course 4, of extracting low-level features and then possibly generating an output of a smaller dimension.

Computationally, the 1-D conv layer also helps speed up the model because now the GRU has to process only 1375 timesteps rather than 5511 timesteps. The two GRU layers read the sequence of inputs from left to right, then ultimately uses a dense+sigmoid layer to make a prediction for y⟨t⟩y^{\langle t \rangle}y⟨t⟩. Because yyy is binary valued (0 or 1), we use a sigmoid output at the last layer to estimate the chance of the output being 1, corresponding to the user having just said "activate."

Step 1: CONV layer. Use Conv1D() to implement this, with 196 filters, a filter size of 15 (kernel_size=15), and stride of 4. []

This creates a dense layer followed by a sigmoid, so that the parameters used for the dense layer are the same for every time step. [.]

Once you've estimated the probability of having detected the word "activate" at each output step, you can trigger a "chiming" sound to play when the probability is above a certain threshold. Further, y⟨t⟩y^{\langle t \rangle}y⟨t⟩ might be near 1 for many values in a row after "activate" is said, yet we want to chime only once. So we will insert a chime sound at most once every 75 output steps. This will help prevent us from inserting two chimes for a single instance of "activate". (This plays a role similar to non-max suppression from computer vision.)

See documentation.
See documentation
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