音频分类，基于音乐数据的分类算法

# AudioNet-V1 AudioNet is a simple convolutional neural net based on 1-D convolutions. This is trained and tested on google's speech command dataset. ## Requirements Tested with following setup ### Software 1) Python 3.5 2) Numpy 3) Scipy 4) Keras 2.0.8 5) Tensorflow 1.4.1 6) Scikit-learn ### Hardware 1) GTX 1050 TI 4 GB ## One Dimensional CNN Here, 1-D convolutions (linear convolutions) are used on top of regular hidden layers to classify the speech signal. The dataset used is Google's speech Commands Dataset The network has five 1-D convolutional layers with kernel size 32 and stride of 4. They are followed by four hidden layers with 512 neurons each. The network has approximately 10 million parameters in total. ### Data Augmentation used 1) Random noise 2) Random shift ### Training Loss vs Epochs  ### Training Acuracy vs Epochs  ## Training The dataset has to be in appropriate subfolders with each folder name being the class label. The script AudioNet32.py needs the following inputs to train 1) data_path : root folder of dataset 2) train_ratio : ratio of files to be used for training and remaining is for validation 3) batch_size : minibatch size for training. 4) num_epochs : total no. of epochs 5) dst : destination folder to save weights, logs The script will generate a pickle file that contains synset for validation, training and validation files path and labels. This can be used to resume training using resume_training() function. The script will save weights once in every 2 epochs. ## Validation The synset used for training is available in train_data_dic.pkl file. The pretrained weights are available in the following link [Download pretrained weights (Epoch 10)](https://drive.google.com/file/d/1vrfeXhtb8mRiLI1ja3Ep80M-zv9a-IVw/view?usp=sharing)