Audio Processing and Feature Building for Machine Learning


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 the same. 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 problem statements 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 is the VP of Data Science at ICX Media, where she mentors her team as they research on natural language processing (NLP), machine learning, feature engineering, distributed computing, and data analytics. She is an inventor of multiple patents in data science, classification and reclassification algorithms, processes and optimizations for media and audience marketing campaigns. Her efforts in driving data science at ICX along with introduction of new methods have strongly contributed to reducing operating costs, securing new clients, and achieving high double digit revenue growth in the past year with positive EBITDA.
Outside her work, she has opened multiple open-source projects and has been a speaker at over a dozen conferences across the globe to share her findings and work with the Python and Data Science community. She is passionate about encouraging women in STEM and volunteers as a mentor at Women Impact Tech and Data Science Nigeria.
She was recognized with Leadership Excellence in Technology by National Diversity Council in 2021 in lieu of her contribution towards the Data Science community.

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