OUR SPEAKERS

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Yama Anin Aminof is a Data Scientist at MyPart, an Israeli startup in the music industry, developing algorithms and researching lyrical and musical song features. She is an activist both in the social world, fighting the violence against women and children, and in the technological world, giving tech talks and mentoring female developers through their first steps in the data science world. Yama has a B.Sc in Mathematics and Physics from Tel Aviv University where she also expresses her passion for music by playing the saxophone in the TAU Wind Band.

Yama Anin Aminof

Data Scientist
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English, Hebrew
Languages:
Location:
Tel Aviv, Israel
Can also give an online talk/webinar

MY TALKS

Can You Sing with All the Voices of the Features?

Data / AI / ML

After this talk, you will know how to write the perfect song for your favourite singer! This is not a songwriting retreat but a talk about song analysis and some of the lyrical, structural, harmonic and melodic features it includes. We will discuss the extraction of lyrical features using NLP (Natural Language Processing) tools; repetition analysis; and how to combine different song structures. We will see what kind of machine learning models we can apply, using all of those features, to predict which songs fit which artist the best. Finally, we will talk about model explainability, which means understanding the results of our models, and introduce an approach that can help us do that. Attend this talk to discover what is the future that the music industry can achieve with machine learning.

Fun With Trees! Get to the Root of Song Classification

Data / AI / ML

Tree-based models are some of the most common machine learning models used today. It makes sense- the basic concept is easy to grasp and easy to work with.
In this talk, I will share my journey with tree-based classifiers while tackling the problem of classifying songs into different genres.
We will dive into the concepts behind the names Decision Trees and XGBoost, and discuss the advantages and disadvantages in comparison to other machine learning models. On the music side, we will discover how to extract features from songs and how to use them to differentiate between genres.
This talk is intended for anyone with basic familiarity with machine learning that would like to deepen their understanding in the subjects of tree-based models, classification, and how to apply machine learning to songs.

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