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https://github.com/sahandevs/dsp-blocks

my DSP playground
https://github.com/sahandevs/dsp-blocks

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my DSP playground

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README 

        
# DSP Blocks



my DSP playground



![demo](./demo.png)



# Todo List



## Phase 1: Foundational DSP and Audio Processing



### Basic Signal Processing



- [x] Blocks Core

- [x] Vis Core

- [x] Implement and visualize basic waveforms (sine, square, sawtooth)

  - [ ] Implement them using fourier approximation

- [ ] Create interactive controls for frequency, amplitude, and phase

- [ ] Implement signal addition, multiplication, and scaling

- [x] [Visualize envelope detection](https://youtu.be/rlypsap6Wow?list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0&t=995)

- [ ] Implement Discrete Fourier Transform (DFT) from scratch

- [ ] Visualize frequency spectrum

- [ ] Compare your DFT with numpy's FFT

- [ ] Create interactive spectrogram visualization

- [ ] Implement simple FIR filters (low-pass, high-pass)

- [ ] Visualize filter frequency response

- [ ] Implement convolution from scratch

- [ ] Create interactive filter design tool



### Advanced DSP Concepts



- [ ] Implement common window functions (Hann, Hamming, Blackman)

- [ ] [Extra-wide window functions](https://signalsmith-audio.co.uk/writing/2021/extra-wide-windows/)

- [ ] Visualize their effect on spectral leakage

- [ ] Implement Short-time Fourier Transform (STFT) from scratch

- [ ] Create interactive spectrogram with adjustable window size and overlap

- [ ] Basic implementation of Wavelet transform

- [ ] Visualize time-frequency resolution differences between STFT and Wavelets

- [ ] Implement basic reverberation

- [ ] Create a simple delay effect

- [ ] Visualize stereo signals



## Phase 2: Music-Specific DSP and Feature Extraction



### Music Fundamentals and Low-Level Features



- [ ] Implement autocorrelation-based pitch detection

- [ ] Visualize pitch over time for monophonic audio

- [ ] Implement energy-based onset detection

- [ ] Visualize onset strength function

- [ ] Implement tempo estimation using autocorrelation

- [ ] Visualize beat locations on waveform

- [ ] Compute and visualize Mel-frequency cepstral coefficients (MFCCs)

- [ ] Implement and visualize chromagram



### Advanced Music Features and Analysis



- [ ] Implement chord recognition using template matching

- [ ] Visualize chord progressions

- [ ] Implement novelty-based segmentation

- [ ] Visualize song structure

- [ ] Implement simple harmonic/percussive source separation

- [ ] Visualize separated components

- [ ] Experiment with unsupervised feature learning (e.g., k-means on spectrograms)

- [ ] Visualize learned features



## Phase 3: Machine Learning for Music



### Basic ML Models for Music Tasks



- [ ] Extract features from a dataset (e.g., GTZAN) for genre classification

- [ ] Implement and evaluate simple models (k-NN, Decision Trees) for genre classification

- [ ] Visualize decision boundaries

- [ ] Collect and preprocess a dataset for mood classification

- [ ] Implement and evaluate SVM for mood classification

- [ ] Visualize feature importance

- [ ] Implement a content-based music recommendation system using extracted features

- [ ] Visualize similarity between songs



### Deep Learning for Music



- [ ] Implement a CNN for genre classification using spectrograms

- [ ] Visualize learned filters and feature maps

- [ ] Implement an LSTM for melody generation

- [ ] Visualize generated melodies

- [ ] Implement a simple autoencoder for audio compression

- [ ] Visualize reconstructed spectrograms



## Phase 4: Advanced Topics and Research



### State-of-the-Art Models and Techniques



- [ ] Implement a basic pitch tracking system for music transcription

- [ ] Visualize transcribed notes on a piano roll

- [ ] Experiment with VAEs or GANs for audio generation

- [ ] Visualize the latent space of generated samples

- [ ] Fine-tune a pre-trained model (e.g., VGGish) for a music task

- [ ] Visualize the activation of different layers



## Phase ?: Random topics to implement



### Effects



- [ ] [Waveshapers](https://signalsmith-audio.co.uk/writing/2022/warm-distortion/)

- [ ] [Limiter](https://signalsmith-audio.co.uk/writing/2022/limiter/)

- [ ] [Reverb](https://signalsmith-audio.co.uk/writing/2021/lets-write-a-reverb/)

- [ ] [Signal Stretch](https://signalsmith-audio.co.uk/writing/2023/stretch-design/) [Vid](https://www.youtube.com/watch?v=fJUmmcGKZMI&feature=youtu.be)

- [ ] [Peak-hold](https://signalsmith-audio.co.uk/writing/2022/constant-time-peak-hold/)

- [ ] [Interpolation](https://signalsmith-audio.co.uk/writing/2021/monotonic-smooth-interpolation/)

- [ ] [Box filter](https://signalsmith-audio.co.uk/writing/2021/box-sum-cumulative/)

  - [ ] [Cascaded box-filter smoothing filters](https://signalsmith-audio.co.uk/writing/2022/cascaded-box-filter-smoothing/)

- [ ] [crossfade](https://signalsmith-audio.co.uk/writing/2021/cheap-energy-crossfade/)



### Synthesis



- [ ] [Analytic pluck synthesis](https://signalsmith-audio.co.uk/writing/2021/analytic-pluck/)



### Visualization Stuff



- [ ] Implement a better colored connections