)
| Titles: Teaching materials will be updated regularly |
Week/date |
Topics |
Additional information |
wav sounds/ Matlab code |
Assignments |
|
| Overview |
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| Ch1: Introduction to audio signal processing | 1(8/9) |
tz1, tz2, sor1, violin3x A4_oboe , trumpet , sor1, A4_violin ,A5_flute |
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| Ch2: Pre-processing of
audio signals |
2 (15/9) |
Time & frequency domain processing, Fourier transform, spectrogram | ||||
| Ch3: Feature
extraction from audio signals |
2 (15/9) |
Filtering, LPC , Cepstral coefficients | ||||
| Ch4: Feature
representation and compression:Vector quantization |
2/3(29/9) |
VQ, K-means | ||||
| Ch5: Speech Recognition |
3(29/9) |
End point detection, per-emphasis, distortion measures, dynamic programming | 5707_revision_Q&A_audio.doc |
Assignment1( released at https://blackboard.cuhk.edu.hk ) Tutorial Files: |
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Ch6: ensemble methods. Classification using Adaboost, |
4(6/10) |
Classifiers, boosting |
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| Ch7: Face detection | 5(13/10) |
Face features, Attentional cascade, |
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| Ch8: Neural Networks |
6(20/10) |
Feed-forward, feed-backward networks, training and recognition | https://sites.google.com/site/hongslinks/tensor_windows |
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Ch9: Convolution Neural Network CNN |
7(27/10) |
Convolution Neural Network CNN | Tensorflow
tutorials 5707_tf_keras.pptx |
Assignment2( To be released
at https://blackboard.cuhk.edu.hk ) |
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| Ch 10.
Auto-encoder |
8(3/11) |
Auto-encoder |
5707_likelihood.pptx |
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| Ch11.
RNN_LSTM |
9(10/11) |
Recurrent Neural Net, Long short-Term Memory | ||||
Ch12. Word representation and seq2seq |
10(17/11) |
Word representation, Machine translation |
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Ch13. Decision tree |
11(24/11) |
Decision tree |
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| Ch14. SVM | 12(1/12) |
Support vector machine | ||||
| Assignment3 (To be released at https://blackboard.cuhk.edu.hk ) |
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| Final exam, see above |
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| Assignment4 (To be released at https://blackboard.cuhk.edu.hk ) |
Important
information
English Video : http://www.cuhk.edu.hk/clear/tnl/Plagiarism_English.html
Cantonese Video : http://www.cuhk.edu.hk/clear/tnl/Plagiarism_Cantonese.html