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Commits on Oct 11, 2024

• fixed typo

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  c1d5b03

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Commits on Oct 5, 2024

• fixed various bugs, migrated to AudioMNIST for digit recognition, re-run all notebooks

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Commits on Oct 4, 2024

• added creation of symlinks to datasets for binder

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• changed dataset to AudioMNIST

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  24bd730

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Commits on Oct 2, 2024

• fixed path for binder service

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  9fa3e64

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Commits on Oct 1, 2024

• updated config for binder

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  660a4ff

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• updated config for binder

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Commits on Jun 18, 2023

• renamed package

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  e8b9a7b

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• deleted config

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  57e07c2

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• moved apt.txt

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  805babf

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• added libsoundfile for binder

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  ad4f6a9

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Commits on Feb 8, 2022

• Added and finalized RNN examples

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  2b8bc65

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• Finalized evaluation and model selection examples

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Commits on Jan 28, 2022

• Fixed dimensions

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  0d5b729

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Commits on Jan 27, 2022

• New examples

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  1b826f9

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• Added new examples

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  281b9a4

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• Fixed condition for early stopping

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  77a956d

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Commits on Jan 20, 2022

• Finalized audio denoising demonstration with CNNs

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  3c7e0fa

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• Audio denosing example using CNNs

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  28efc6a

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• Added audio files

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  06e3137

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• Merge branch 'dev' of github.com:spatialaudio/data-driven-audio-signal-processing-lecture into dev

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• Demonstration of audio denoising using a fully-connected deep network

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• Demonstration auf audio denoising by DNN

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  68730be

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• Directory for CNN examples

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Commits on Jan 19, 2022

• Demonstration auf audio denoising by DNN

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  6f7dc0f

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Commits on Jan 13, 2022

• Fixed link

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  5598419

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• Added example showing pattern detection with matched filters

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  708d5ee

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• Added example on spoken digit classification using CNNs

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  ceebf2e

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• Added example using a convolutional layer for LTI system identification

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Commits on Jan 7, 2022

• Notebook on function approximation capabilities of DNNs, tensorflow update

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  5709a43

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• Illustration of function approximation capabilities of DNNs

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  7503e44

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Commits on Jan 6, 2022

• Added DNN classification examples

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Commits on Jan 4, 2022

• Example of audio digits classification by DNN

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Commits on Dec 16, 2021

• Demonstration of system identification using multivariate gradient descent

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• Demonstration of univariate regression using mini-batch gradient descent

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