Since you’re reading a blog post from a technology company, I bet you’ve heard about AI, Machine and Deep Learning many times before.
Audio or sound classification is a technique with c level contact list multiple applications in the field of AI and data science.
Use cases
- Acoustic data classification:
– identifies location
– differentiates environments
– has a role in ecosystem monitoring
- Environmental sound classification:
– recognition of urban sounds
– used in security system
– used in predictive maintenance
– used to differentiate animal sounds
- Music classification:
– classify music
– key role in: audio libraries organisation by genre, improvement of recommandation algorithms, discovery of trends, listener preferences through data analysis, …
- Natural language classification:
– human speech classification
– common in: chatbots, virtual assistants, tech-to-speech application, …
In this article we will look at the classification of marine mammal sounds.
Objective
The purpose of this article is to explain how to train a model to classify audios using AI Notebooks.
In this tutorial, the sounds in the dataset these memories of johannes are in .wav format. To be able to use them and obtain results, it is necessary to pre-process this data by following different steps.
- Analyse one of these audio recordings
- Transform each sound file into a .csv file
- Train your model from the .csv file
USE CASE: Best of Watkins Marine Mammal Sound Database
This dataset is composed of 55 different folders corresponding to the marine mammals. In each folder are stored several sound files of each animal.
You can get more information about this dataset on this website.
The data distribution is as follows:
Let’s follow the different steps!
Loading an audio file with Librosa
Librosa is a Python module for audio signal analysis. By using Librosa, you can extract key features from the audio samples such as Tempo, Chroma aero leads Energy Normalized, Mel-Freqency Cepstral Coefficients, Spectral Centroid, Spectral Contrast, Spectral Rolloff, and Zero Crossing Rate. If you want to know more about this library, refer to the documentation.