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Code for YouTube series: Deep Learning for Audio Classification
| Date | Stars |
|---|---|
| 2026-07-31 | 588 |
| 2026-08-02 | 588 |
| 2026-08-06 | 588 |
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# Audio-Classification (Kapre Version) Pipeline for prototyping audio classification algorithms with TF 2.3  <!-- TOC --> - [YouTube](#youtube) - [Environment](#environment) - [Jupyter Notebooks](#jupyter-notebooks) - [Audio Preprocessing](#audio-preprocessing) - [Training](#training) - [Plot History](#plot-history) - [Confusion Matrix](#confusion-matrix) - [Receiver Operating Characteristic](#receiver-operating-characteristic) - [Kapre](#kapre) <!-- /TOC --> ### YouTube This series has been re-worked. There are new videos to support this repository. It is recommended to follow the new series. https://www.youtube.com/playlist?list=PLhA3b2k8R3t0SYW_MhWkWS5fWg-BlYqWn If you want to follow the old videos, restore to a previous commit. `git checkout 404f2a6f989cec3421e8217d71ef070f3593a84d` ### Environment ``` conda create -n audio python=3.7 activate audio pip install -r requirements.txt ``` ### Jupyter Notebooks Assuming you have ipykernel installed from your conda environment `ipython kernel install --user --name=audio` `conda activate audio` `jupyter-notebook` ### Audio Preprocessing clean.py can be used to preview the signal envelope at a threshold to remove low magnitude data When you uncomment split_wavs, a clean directory will be created with downsampled mono audio split by delta time `python clean.py`  ### Training Change model_type to: conv1d, conv2d, lstm Sample rate and delta time should be the same from clean.py `python train.py` ### Plot History Assuming you have ran all 3 models and saved the images into logs, check `notebooks/Plot History.ipynb`  `notebooks/Confusion Matrix and ROC.ipynb` ### Confusion Matrix  ### Receiver Operating Characteristic  ### Kapre For computation of audio transforms from time to frequency domain on the fly https://github.com/keunwoochoi/kapre https://arxiv.org/pdf/1706.05781.pdf
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matched fp:82ad0a261621a73b, llm:Repository topics: audio-classification, kapre, keras, tensorflow2, youtube. Description and README: 'Deep Learning for Audio Classification', notebooks and scripts for preprocessing, training conv1d/conv2d/lstm models, Kapre for audio transforms.
matched fp:82ad0a261621a73b, llm:Repository topics: audio-classification, kapre, keras, tensorflow2, youtube. Description and README: 'Deep Learning for Audio Classification', notebooks and scripts for preprocessing, training conv1d/conv2d/lstm models, Kapre for audio transforms.
matched fp:82ad0a261621a73b, llm:Repository topics: audio-classification, kapre, keras, tensorflow2, youtube. Description and README: 'Deep Learning for Audio Classification', notebooks and scripts for preprocessing, training conv1d/conv2d/lstm models, Kapre for audio transforms.