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![Audio representations
Type Description
Data shape and size
for e.g., 1 second,
sampling rate=44100
Waveform x
44100 x [int16]
Spectrograms
STFT(x)
Melspectrogram(x)
CQT(x)
513 x 87 x [float32]
128 x 87 x [float32]
72 x 87 x [float32]
Features
MFCC(x)
= some process on STFT(x)
20 x 87 x [float32]
Spoiler: log10(Melspectrograms) for the win,
but let's see some details](https://image.slidesharecdn.com/qconkeunwoochoi-190609193832/85/Deep-Learning-with-Audio-Signals-Prepare-Process-Design-Expect-16-320.jpg)
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Deep Learning with Audio Signals: Prepare, Process, Design, Expect
- 1. Deep Learning with AudioSignals Prepare, Process, Design, Expect Keunwoo Ch i
- 2. Keunwoo Choi QMUL, UK ETRI,S. Korea SNU, S. Korea @keunwoochoi (twtr, github) Research Scientist
- 3. WARNING THIS MATERIAL ISWRITTEN FOR ATTENDEES IN QCON.AI, NAMELY, SOFTWARE ENGINEERS AND DEEP LEARNING PRACTITIONERS TO PROVIDE AN OFF-THE- SHELF GUIDE. MY ADVICE MIGHT NOT BE THE FINAL SOLUTION FOR YOUR PROBLEM, BUT WOULD BE A GOOD STARTING POINT. ..ALSO, THERE'S NO SPOTIFY SECRET HERE :P
- 4. Content • Prepare thedataset • Pre-process the signal • Design your network • Expect the result
- 5. Prepare the datasets or,know your data Q. How to start an audio task?
- 6. LMGTFY • Google them,of course • But....
- 7. Audio dataset • Lucky→ the exactly same class(es), many of them, yay! • Meh → same or similar classes, sounds alright.. • Ugh.. → there are 2 in freesound.org and 3 on youtube
- 8. Audio (or, sound)dataset • Our algorithm is living in the digital space • So is the .wav files • But, the sound is in the real world Our lovely cyberspace
- 9. Audio dataset Source Noise Reverberation Microphone • Roomreverberation image from https://johnlsayers.com/Recmanual/Pages/Reverb.htm
- 10. Audio dataset Dear everyone, YOUARE ALWAYS IN THE "UGH..." SITUATION → HOW TO BUILD A CORRECT AUDIO DATASET?
- 11. What we cando • Know your real situation • You can mimic noise/reverberation/mic if you have • clean/dry/high-quality source signals DL models are robust only within the variance they've seen. → Good at interpolation.. only. E.g., a model trained with clean signals probably can't deal with noisy signals noisy environment cheap mic
- 12. Simulate the realworld + noise signalclean signal noisy signal room impulse responsedry signal wet signal band-pass filter original signal recorded signal
- 13. What to Google Noise babblenoise recording home noise recording cafe noise recording street noise recording white noise, brown noise x_noise = x + alpha * noise Reverberation (maybe skip it) room impulse responses, RIR reverberation simulators x_wet = np.conv(x, rir) Microphone band pass filter scipy.signal filtering microphone specification speaker specification microphone frequency response scipy.signal.convolve scipy.signal.fftconvolve Or trimming-off your spectrograms
- 14. Pre-process the signals or,log(melgram) Q. What to do after loading the signals?
- 15. Digital Audio 101 •1 second of digital audio: size=(44100, ), dtype=int16 • MNIST: (28, 28, 1), int8 CIFAR10: (32, 32, 3), int8 ImageNet: (256, 256, 3), int8 • Audio: Lots of data points in one item!
- 16. Audio representations Type Description Datashape and size for e.g., 1 second, sampling rate=44100 Waveform x 44100 x [int16] Spectrograms STFT(x) Melspectrogram(x) CQT(x) 513 x 87 x [float32] 128 x 87 x [float32] 72 x 87 x [float32] Features MFCC(x) = some process on STFT(x) 20 x 87 x [float32] Spoiler: log10(Melspectrograms) for the win, but let's see some details
- 17. Spectrograms • 2-dim representationof audio signal TODO: IMAGE
- 18. Practitioner's choice • Ruleof thumb: DISCARD ALL THE REDUNDANCY • Sample rate, or bandwidth • Goal: To optimize the input audio data for your model • by resampling - can be computation heavier • by discarding some freq bands - can be storage heavy https://www.summerrankin.com/dogandponyshow/2017/10/16/catdog
- 19. Practitioner's choice • Melspectrogram -in decibel scale - which only covers the frequency range you're interested in. • Why? - smaller, therefore easier and faster training - perceptual - weighing more on the freq region where humans are more interested - faster than CQT to compute - decibel scale - another perceptually motivated choice Q. Ok, how can I compute them?
- 20. import librosa import madmom •Python libraries - librosa/madmom/scipy/.. • Computations on CPU • Best when all the processing will be done before the training
- 21. import kapre • KerasAudio Preprocessing layers • CPU and GPU • Best when you want to do things on the fly/GPU = Best to optimize audio-related parameters • pip install kapre • There's also pytorch-audio!Disclaimer: I'm the maintainer
- 22. Design your network or,know the assumptions Q. What kind of network structure I need?
- 23. A dumb-but-strong-therefore-good-while- annoying-since-it's-from-computer-vision baseline approach •Trim the signals properly (e.g. 1-sec) • Do the classification with 2D convnet, 3x3 kernel (=aka vggnet) • Raise $1B • Retire • Post "why i retired.." on Medium • Happy life!
- 24. Go even dumber •Just download some pre-trained networks for.. - music - audio - image (?) • Re-use it for your task (aka transfer learning) • 1B - retire - Medium - happy - repeat
- 25. Better and stronger, byunderstanding assumptions • assert "Receptive field" size == size of the target pattern • How sparse the target pattern is? - Bird singing sparse? - Voice-in-music sparse? - Distortion-guitar-in-Metallica sparse?
- 26. Have no idea? •Go see how computer vision people are doing • Clone it • It's ok, it's a good baseline at least
- 27. My spectrogram is28x28 bc the model I downloaded is trained on MNIST Don't use spectrograms as if they are images It all boils down to the pattern recognition, they're actually similar tasks. the time and frequency axes have totally different meanings I don't know how to incorporate them into my model.. BUT IT WORKS!
- 28. Expecting the result or,know the problem Q. How would it work?
- 29. YOU • You areresponsible for the feasibility • Is it a task you can? • Is the information in the input (mel-spectrogram)? • Are similar tasks being solved?
- 30. Think about it! •Is it possible? To what extent? E.g., • Baby crying detection • Baby crying recognition and classification • Dog barking translation • Hit song detection
- 31.
- 32. Conclusion • Sound isanalog, you might need to think about some analog process, too. • Pre-process: Follow others when you're lost • Audio is big in data size, but sparse in information. Reduce the size. Don't start with end-to-end. • Design: Follow others when you're lost • Expect: Make sure if it's doable
- 33. Deep Learning with AudioSignal Prepare, Process, Design, Expect Keunwoo Ch i Q&A PS. See you soon at the panel talk!