## Digital Signal Processing and Deep Learning
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This repository is a study of **Digital Signal Processing techniques** for **Audio** signals
1. DSP helps creation of **features** for ML
- We look at creation of features for traditional ML - for example a SVM classifer
- And, we look at creating features for **Deep learning** techniques such as MLP, CNN, RNN and LSTM
2. For _traditional_ ML feature engineering, we look at Time domain, Frequency domain and time-frequency features
3. **Time Domain features**:
- A-D-S-R model: Attack-Decay-Sustain-Release model for audio
- Amplitude envelope (AE)
- Root-mean-square Energy (RMS-E)
- Zero-crossing rate (ZCR)
4. **Frequency Domain features**:
- FFT - Fast Fourier Transform
- STFT - Short Time Fourier Transform
- MFCC - Mel Frequency Cepstral Coefficents
- MFCC: MFCC feature extraction technique basically includes windowing the signal,
applying the DFT, taking the log of the magnitude, and then warping the frequencies
on a Mel scale, followed by applying the inverse DCT.
5. For deep learning, we use MFCC converted to images
6. We build a CNN using TensorFlow Keras
7. To-Do: Build RNN, LSTM for DSP
8. For additional technical notes please see: https://github.com/Rajesh-Siraskar/Digital_Signal_Processing_and_Deep_Learning/blob/main/README_DSP-Notes.ipynb
## Project
#### Valve fault diagnotics based on sound files
- 20-May-2021
- Attempt to train to recognize faulty valves
- Unseen data will be from another folder