BECCA listens for sound effects in The Hobbit

Brandon Rohrer · Beginner ·📐 ML Fundamentals ·12y ago

Key Takeaways

The video demonstrates BECCA, a machine learning model, listening for sound effects in an audio clip from The Hobbit, showcasing its ability to recognize and respond to specific sounds.

Full Transcript

The Bodyguard of bulg came howling against them and drove in upon their ranks like waves upon Cliffs of sand their friends could not help them for the assault from the mountain was renewed with redoubled force and upon either side men and elves were being slowly beaten down on all this bbo looked with misery he had taken his stand on Raven Hill among the elves partly because there was more chance of escape from that point and partly with a more tokish part of his mind because if he was going to be in a last desperate stand he preferred on the whole to defend the Elven King Gandalf too I may say was there sitting on the ground as if in deep thought preparing I suppose some last blast of magic before the end that did not seem far off it will not be long now thought Bilbo before the Goblins win the gate and we're all slaughtered or driven down and captured really it's enough to make one weep after all one's gone through I would rather old smug had been left with all the wretched treasure than that these vile creatures should get it poor old bomber and Bar daring and fely and key and all the rest come to a bad end and Bard too and the lake men and the merry misery me I've heard songs of many battles and I've always understood that defeat may be glorious it seems very uncomfortable not to say distressing I wish I was well out of it the clouds were torn by the wind and a red sunset slashed the West seeing the sudden gleam in the Gloom Bilbo looked round he gave a great yes what is it cry he had seen a sight that made his heart leap dark shaped small yet Majestic against the distant glow the Eagles wow the Eagles he shouted the Eagles are coming Bilbo's eyes were seldom wrong yikes the Eagles were coming down the wind line after line in such a host as must have gathered from all thees of the north the Eagles the E Eagles Bilbo cried dancing and waving his arms if the elves could not see him him they could hear him soon they too took up the cry and it echoed across the valley many wondering eyes looked up though was yet nothing could be seen except from the southern shoulders of the mountain the Eagles cried Bilbo once more but at that moment a stone hurtling from above smote heavily on his Helm and he fell with a crash and knew no more

Original Description

Video abstract: http://www.youtube.com/watch?v=EV7Rg1Jono4&list=PLF861CC4C40439EEB Full audio feature set: http://youtu.be/rmv6Pox_8CI Code and documentation: https://github.com/brohrer/ BECCA users group: https://groups.google.com/forum/?fromgroups#!forum/becca_users BECCA community: http://www.openbecca.org A general machine learning algorithm processed the audio data from a narrated version of The Hobbit, interspersed with sound effects. Because it had previously listened to 48 hours of The Hobbit without the sound effects, it was able to identify them as anomalies. It created a hierarchy of tonal-temporal features unsupervised, as well as a model of their occurrence. Based on this, BECCA determined the novelty of the input and identifies unusual audio events.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Brandon Rohrer · Brandon Rohrer · 6 of 60

1 Robot Learning with a Biologically-Inspired Brain (BECCA)
Robot Learning with a Biologically-Inspired Brain (BECCA)
Brandon Rohrer
2 BECCA talk at AGI 2011
BECCA talk at AGI 2011
Brandon Rohrer
3 Robot Learning with a Biologically-Inspired Brain (BECCA), The Sequel
Robot Learning with a Biologically-Inspired Brain (BECCA), The Sequel
Brandon Rohrer
4 BECCA listens to The Hobbit
BECCA listens to The Hobbit
Brandon Rohrer
5 Learning the building blocks of speech: BECCA extracts a hierarchy of audio features
Learning the building blocks of speech: BECCA extracts a hierarchy of audio features
Brandon Rohrer
BECCA listens for sound effects in The Hobbit
BECCA listens for sound effects in The Hobbit
Brandon Rohrer
7 BECCA finds movie trailers while watching the Big Bang Theory
BECCA finds movie trailers while watching the Big Bang Theory
Brandon Rohrer
8 Listening for unexpected sounds: BECCA detects anomalies in audio data
Listening for unexpected sounds: BECCA detects anomalies in audio data
Brandon Rohrer
9 Learning the building blocks of vision: BECCA extracts a spatio-temporal hierarchy of features
Learning the building blocks of vision: BECCA extracts a spatio-temporal hierarchy of features
Brandon Rohrer
10 Watching for the unexpected: BECCA detects anomalies in video data
Watching for the unexpected: BECCA detects anomalies in video data
Brandon Rohrer
11 BECCA finds a stationary target
BECCA finds a stationary target
Brandon Rohrer
12 BECCA finds a stationary target at 3X speed
BECCA finds a stationary target at 3X speed
Brandon Rohrer
13 BECCA watches the X-men and Bruce Lee
BECCA watches the X-men and Bruce Lee
Brandon Rohrer
14 BECCA plays Quidditch
BECCA plays Quidditch
Brandon Rohrer
15 BECCA chases a ball
BECCA chases a ball
Brandon Rohrer
16 BECCA chases a ball, part 2
BECCA chases a ball, part 2
Brandon Rohrer
17 Becca chases a ball, part 3
Becca chases a ball, part 3
Brandon Rohrer
18 BECCA creates features from MNIST
BECCA creates features from MNIST
Brandon Rohrer
19 How reinforcement learning works in Becca 7
How reinforcement learning works in Becca 7
Brandon Rohrer
20 Deep Learning Demystified
Deep Learning Demystified
Brandon Rohrer
21 How Data Science Works
How Data Science Works
Brandon Rohrer
22 How Convolutional Neural Networks work
How Convolutional Neural Networks work
Brandon Rohrer
23 How Bayes Theorem works
How Bayes Theorem works
Brandon Rohrer
24 How Deep Neural Networks Work
How Deep Neural Networks Work
Brandon Rohrer
25 Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
Brandon Rohrer
26 How Support Vector Machines work / How to open a black box
How Support Vector Machines work / How to open a black box
Brandon Rohrer
27 How autocorrelation works
How autocorrelation works
Brandon Rohrer
28 Getting closer to human intelligence through robotics
Getting closer to human intelligence through robotics
Brandon Rohrer
29 A minimalist's guide to slicing and indexing pandas DataFrames
A minimalist's guide to slicing and indexing pandas DataFrames
Brandon Rohrer
30 How decision trees work
How decision trees work
Brandon Rohrer
31 Data scientist archetypes
Data scientist archetypes
Brandon Rohrer
32 How to use python's datetime package
How to use python's datetime package
Brandon Rohrer
33 How optimization for machine learning works, part 1
How optimization for machine learning works, part 1
Brandon Rohrer
34 How optimization for machine learning works, part 2
How optimization for machine learning works, part 2
Brandon Rohrer
35 How optimization for machine learning works, part 3
How optimization for machine learning works, part 3
Brandon Rohrer
36 How optimization for machine learning works, part 4
How optimization for machine learning works, part 4
Brandon Rohrer
37 How convolutional neural networks work, in depth
How convolutional neural networks work, in depth
Brandon Rohrer
38 How to pick a machine learning model 4: Splitting the data
How to pick a machine learning model 4: Splitting the data
Brandon Rohrer
39 How to pick a machine learning model 3: Choosing a loss function
How to pick a machine learning model 3: Choosing a loss function
Brandon Rohrer
40 How to pick a machine learning model 2: Separating signal from noise
How to pick a machine learning model 2: Separating signal from noise
Brandon Rohrer
41 How to pick a machine learning model 1: Choosing between models
How to pick a machine learning model 1: Choosing between models
Brandon Rohrer
42 How to pick a machine learning model 5: Navigating assumptions
How to pick a machine learning model 5: Navigating assumptions
Brandon Rohrer
43 What do neural networks learn?
What do neural networks learn?
Brandon Rohrer
44 Interview with iRobot's Director of Data Science Angela Bassa
Interview with iRobot's Director of Data Science Angela Bassa
Brandon Rohrer
45 How Backpropagation Works
How Backpropagation Works
Brandon Rohrer
46 Evolutionary Powell's method: A discrete optimizer for hyperparameter optimization
Evolutionary Powell's method: A discrete optimizer for hyperparameter optimization
Brandon Rohrer
47 1D convolution for neural networks, part 1: Sliding dot product
1D convolution for neural networks, part 1: Sliding dot product
Brandon Rohrer
48 1D convolution for neural networks, part 2: Convolution copies the kernel
1D convolution for neural networks, part 2: Convolution copies the kernel
Brandon Rohrer
49 1D convolution for neural networks, part 3: Sliding dot product equations longhand
1D convolution for neural networks, part 3: Sliding dot product equations longhand
Brandon Rohrer
50 1D convolution for neural networks, part 4: Convolution equation
1D convolution for neural networks, part 4: Convolution equation
Brandon Rohrer
51 1D convolution for neural networks, part 5: Backpropagation
1D convolution for neural networks, part 5: Backpropagation
Brandon Rohrer
52 1D convolution for neural networks, part 6: Input gradient
1D convolution for neural networks, part 6: Input gradient
Brandon Rohrer
53 1D convolution for neural networks, part 7: Weight gradient
1D convolution for neural networks, part 7: Weight gradient
Brandon Rohrer
54 1D convolution for neural networks, part 8: Padding
1D convolution for neural networks, part 8: Padding
Brandon Rohrer
55 1D convolution for neural networks, part 9: Stride
1D convolution for neural networks, part 9: Stride
Brandon Rohrer
56 The Four Grand Challenges of Robots in the Home
The Four Grand Challenges of Robots in the Home
Brandon Rohrer
57 How Convolution Works
How Convolution Works
Brandon Rohrer
58 The Softmax neural network layer
The Softmax neural network layer
Brandon Rohrer
59 Batch normalization
Batch normalization
Brandon Rohrer
60 Getting ready to learn Python, Mac edition #1: Files and directories
Getting ready to learn Python, Mac edition #1: Files and directories
Brandon Rohrer

This video teaches how to use BECCA to listen for sound effects in an audio clip from The Hobbit, demonstrating its potential applications in audio processing and natural language processing. The video provides a beginner-friendly introduction to machine learning fundamentals and audio processing basics. By following this lesson, viewers can learn how to build and train their own ML models for audio processing tasks.

Key Takeaways
  1. Install BECCA and its dependencies
  2. Prepare an audio clip for processing
  3. Train a model on labeled data
  4. Test the model on a new audio clip
  5. Fine-tune the model for better performance
💡 The video highlights the importance of high-quality training data and proper model tuning for achieving accurate results in audio processing tasks.

Related AI Lessons

Up next
Learn Deep Learning by Hand (Beginner's Guide - Part 1)
Thu Vu
Watch →