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[𝛍1, 𝛍2, 𝛍3, 𝛍4]](https://image.slidesharecdn.com/machinelearningformusic-150409105357-conversion-gate01/85/Machine-learning-for-Music-48-320.jpg)
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(defn deep-learning? [neural-net]
(hidden-layer? neural-net))
we are trying to learn new high-level representation having
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Uploaded byPetko Nikolov
Machine learning for Music
This document discusses machine learning techniques for music information retrieval. It provides an overview of music recommendation systems like Spotify's shuffle mode and Pandora's music genome project. Key music information retrieval tasks are identified like genre recognition, mood detection, and audio similarity. Machine learning architectures for music information retrieval are examined including feature extraction from audio, classification with neural networks, and deep learning techniques like convolutional neural networks and autoencoders.
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Machine learning for Music
- 1. Machine Learning for Music Facultyof Mathematics and Informatics, SU Petko Nikolov April 8, 2015
- 2. About Me Machine Learning MusicInformation Retrieval Machine Learning / Automated Data Science
- 3. What’s Music InformationRetrieval? Musicology Computer Science Signal Processing Machine Learning MIR
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- 8. Spotify’s Shuffle Mode ●Not really random ● Certainly some processing ● Probably some MIR behind
- 9. Pandora’s Music GenomeProject ● started in 2000 ● 800 000 manually annotated tracks by music experts ● 450 attributes to describe music ● 25 minutes per track to label
- 10. MIREX Music Information RetrievalEvaluation eXchange annual competition featuring more than 20 tasks state-of-the-art algorithms compete against each other
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- 12. fingerprinting cover song detection genrerecognition instrument recognition mood detection transcription playlist generation beat tracking key detection pitch tracking vocal detection recommendation audio similarity source separation
- 13. genre recognition instrument recognition mooddetection vocal detection audio similarity
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- 19. Segmentation and Preprocessing classical piano romantic Bethoven Deep Learning by DanielBarenboim 2 4 MIR Architecture Audio
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- 21. Audio signal human hearing:20 Hz to 20 KHz
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- 29. Segmentation Frame 52 ms f1 f2f3 f4 fn
- 30. Spectrum - onframe level Discrete Fourier Transform (DFT) time frequency
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- 32. Spectral Centroid where isthe ‘center of mass’ of the spectrum
- 33. Spectral Slope fit linearregression and get the slope coef.
- 34. Spectral Slope fit linearregression and get the slope coef.
- 35. Spectral Slope fit linearregression and get the slope coef.
- 36. Spectral Slope fit linearregression and get the slope coef.
- 37. Spectral Correlation isthe cosine distance between the frequency vectors of two consecutive frames Variation is (1.0 - correlation) respectively. Spectral Correlation / Variation
- 38. Feature extraction -Result f11 f12 f13 f14 f15 ……… f1m f21 f22 f23 f24 f25 ……… f2m centroid correlation Frames
- 39. Feature extraction -Result f11 f12 f13 f14 f15 ……… f1m f21 f22 f23 f24 f25 ……… f2m centroid correlation Frames frames number vary across audio recordings
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- 41. Gaussian Mixture Model framefeature vector
- 42. Gaussian Mixture Model MultivariateGaussian Distribution
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- 45. Gaussian Mixture Model- per track
- 46. Gaussian Mixture Model- per track
- 47. Gaussian Mixture Model- per track
- 48. Gaussian Mixture Model- per track [𝛍1, 𝛍2, 𝛍3, 𝛍4]
- 49. Classification - ExampleNeural Net aik wk Feature vector Input Hidden Output Likelihood of Rock? Layers:
- 50. Classification - ExampleNeural Net aik wk Feature vector Input Hidden Output Likelihood of Rock? Layers:
- 51. Classification - ExampleNeural Net aik wk Feature vector Input Hidden Output Likelihood of Rock? Layers:
- 52. What’s Deep Learning? (defndeep-learning? [neural-net] (hidden-layer? neural-net)) we are trying to learn new high-level representation having many more hidden layers input is as raw as possible
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- 57. Deep Neural Network Backpropagationgradient fades quickly
- 58. Deep Belief Network Input(Mel spectrum) Output Hidden Layer 3 Hidden Layer 2 Hidden Layer 1Restricted Boltzmann Machine RBM RBM RBM Rock Jazz Punk Electronic
- 59. Deep Belief Network Input(Mel spectrum) Hidden Layer 1Restricted Boltzmann Machine
- 60. Deep Belief Network Input(Mel spectrum) Hidden Layer 1Restricted Boltzmann Machine
- 61. Deep Belief Network Input(Mel spectrum) Output Hidden Layer 3 Hidden Layer 2 Hidden Layer 1Restricted Boltzmann Machine RBM RBM RBM Rock Jazz Punk Electronic
- 62. Deep Auto Encoders Melspectrum Mel spectrumOutput Input
- 63. Deep Auto Encoders Melspectrum Mel spectrumOutput Input Used for denoising
- 64. Tools essentia - audioretrieval algorithms theano - CPU/GPU symbolic optimization scikit-learn - machine learning in Python