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OUR SPEAKERS
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Yama Anin Aminof is a Data Scientist at Meta, fighting fraud in financial services. In her previous role, she worked 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, as a public speaker and a mentor to women taking 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
English, Hebrew
Languages:
Location:
Tel Aviv, Israel
Can also give an online talk/webinar
Paid only. Contact speaker for pricing!
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.
Living in Perfect Harmony - Where Music and Machine Learning Meet
Data / AI / ML
The revolution of machine learning is reaching every aspect of our lives - including art and music.
In this talk, we will dive into the world of song analysis and the extraction of lyrical and musical features. We will discuss existing approaches, both in machine learning - Natural Language Processing, Digital Signal Processing - and in music theory & linguistics. Next, we will see how we can use these features in different kinds of machine learning models, and how these models can be used to solve problems in the music industry, such as song tags and song similarity.
Attend this talk to learn how your technical skills can be useful also to your hobbies.
Can You Sing with All the Voices of the Features?
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Fun With Trees! Get to the Root of Song Classification
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Living in Perfect Harmony - Where Music and Machine Learning Meet
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