UrbanSound8K

Urban Sound 8K Classification Using CNN

Dataset and Its Structure

1. Urban Sound Classification Dataset: The Urban Sound Classification dataset is a popular dataset widely used in sound classification tasks. It can be accessed through the official website.

2. Understanding Dataset Structure and Feature Extraction:

   • Regardless of the dataset used, it's crucial to comprehend its structure and methods for extracting necessary features.

3. Downloading UrbanSound8K Dataset:

   • The UrbanSound8K dataset can be downloaded from this link. Upon downloading, it yields a compressed tar file of approximately 6GB in size.

4. Dataset Contents:

   • Upon extraction, the dataset comprises two main folders: 'audio' and 'metadata'.

5. Audio Folder:

   • The 'audio' folder contains 10 subfolders named fold1, fold2, and so forth, each containing around 800 audio files, each lasting 4 seconds.

6. Metadata Folder:

   • The 'metadata' folder contains a CSV file with various columns such as file_id, label, class_id corresponding to the label, salience, etc.

7. Detailed Description:

   • More detailed information about the dataset structure and its contents can be found here.

Research Papers and Resources to Follow

1. Meyda
2. pyAudioAnalysis
3. Speech Recognition with Deep Learning
4. Urban Sound Classification Part 1
5. Audio & Voice Processing with Deep Learning

Recommended Library

• Librosa Library: This library can be installed using the following commands:

  pip install librosa
  pip install ffmpeg-python
  

  Librosa facilitates reading audio files and converting them into amplitude values for each sample. For instance, a 4-second audio file with a sampling rate of 22050 Hz translates to an array of size 88200, with each element representing an amplitude sample.