This project classifies sound signals from different environmental classes in the ESC-10 dataset. the above photo summarizes the model steps:

1. The model read all the signals of different classes and assign a label number to each class.
2. The signal is converted from the time domain to Wavelet Transform. PCA technique is used to reduce dimensions of wavelet transform as it contain a lot of dimensions.
3. Full Convolutional Neural Network(CNN) is defined and used to classify 10 different classes of ESC-10 dataset.

1. download the ESC-10 dataset from this link: ESC-10

2. change the directory name that contains the dataset to the name in the notebook file or change the name in the notebook file in these three lines:
   

#here my directory name is "dataset"

data, samplerate = librosa.load("dataset/dog/1-30344-A.wav", sr=44000) 

for filepath in glob.iglob('dataset/*'):

for j in glob.iglob('dataset/'+i+'/*'):

• numpy
• keras
• matplotlib
• librosa
• pylab
• glob
• tensorflow
• scipy
• pywt

For more details on wavelet transform and how to deal with it see this course