Classifying Audio into Types using Python

Unlike types of data that are more commonly dealt with in the industry these days, such as numerical data, text or image data, audio signals need a different approach while trying to extract information and building machine learning models. This talk will highlight the challenges with Audio Classification problems starting with what an audio signal is and what its numerical representation means, how it is widely different from other data types, what feature extraction from audio looks like, how to go about it, what it means and the open source tools in Python that can be leveraged for solving an end-to-end audio classification problem. Digital signal processing, that includes audio processing, is a whole separate field to study and leveraging portions of learning from that in order to build successful models on audio data is an interesting and challenging problem. In addition, Matlab is a popular language of choice with great tools for audio signal processing. Python being a popular language of choice for Machine Learning presents another set of challenges to build successful audio and speech classification solutions in Python alone. Focus will then upon how to build classification models from the features representing the unseen information from audio and speech signals and doing it all leveraging different open source tools available to Python users. This will be followed by a few examples of different audio classification and prediction tasks and a solution for attempting to solve them using Python using the different features formation techniques and tools discussed earlier in the talk.

Jyotika Singh

Jyotika Singh is the Director of Data Science at ICX, where she and her team work on NLP, feature engineering, supervised and unsupervised machine learning, research, data analytics, programming in Python and distributed computing with Spark. She is passionate about solving problems using the power of Data and Machine Learning.

She earned her Master's in Science degree from the University of California, Los Angeles where she researched on signal and speech processing, developed novel approaches to remove noise from speech and worked on a variety of machine learning projects on image, text, user ratings, social media, entertainment and movies data. ​ Outside her work, she enjoys working on a variety of problem solving techniques on text, audio and image data, has opened multiple github open source projects, such as pyAudioProcessing and pyYouTubeAnalysis, to share her findings and work with the Python and Data Science community. ​ In her free time, she is big on spending time with family and friends, painting, art & decor, and trying out different sports.