Commit 60c067fc authored by Simon's avatar Simon
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modified readme

parent 4d6f8af2
......@@ -64,11 +64,11 @@ In order to guide you in this project, you have access to the following Jupyter
* how to read, write, play, and visualize audio files;
* how to compute a log-Mel spectrogram from a raw audio waveform.
* `3-feature-extraction.ipynb`: In this notebook, you will extract the log-Mel spectrograms for the 3068 audio files in the SONYC-UST dataset.
* `3-feature-extraction.ipynb`: In this notebook, you will extract the log-Mel spectrograms for the 3068 audio files in the SONYC-UST dataset. It may take a significant amount of time, so anticipate!
* `4-model-training.ipynb`: In this notebook, you will build and train a convolutional neural network (CNN) to perform urban sound tagging, using [Keras](https://keras.io/).
* `4-model-training.ipynb`: In this notebook, you will build and train a convolutional neural network (CNN) to perform urban sound tagging with [Keras](https://keras.io/). Using transfer learning, your CNN will build upon a model called [VGGish](https://github.com/tensorflow/models/tree/master/research/audioset/vggish). It was trained on [AudioSet](https://github.com/tensorflow/models/tree/master/research/audioset), a dataset of over 2 million human-labeled 10-second YouTube video soundtracks, with labels taken from an ontology of more than 600 audio event classes. This represents more than 5 thousand hours of audio. The method you will implement is based on ["Convolutional Neural Networks with Transfer Learning for Urban Sound Tagging"](http://dcase.community/documents/challenge2019/technical_reports/DCASE2019_Kim_107.pdf) that was proposed by Bongjun Kim (Department of Computer Science, Northwestern University, Evnaston, Illinois, USA) and obtained the 3rd best score at the [DCASE 2019 Challange, task 5](http://dcase.community/challenge2019/task-urban-sound-tagging).
* `5-model-testing.ipynb`: In this notebook, you will evaluate the performance of your trained CNN using standard metrics for multi-label classification.
* `5-model-testing.ipynb`: In this notebook, you will evaluate the performance of your trained CNN using standard metrics for [multi-label classification](https://en.wikipedia.org/wiki/Multi-label_classification).
* `6-jetson-nano.ipynb`: In this notebook, you will learn some tools to help you embed your trained urban sound tagging system on the [Nvidia Jetson Nano](https://developer.nvidia.com/embedded/jetson-nano-developer-kit).
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