Music


Modern RecSys: How does a recommender work?

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I have worked in the data industry for over seven years and had the privilege of designing, building, and deploying two recommender systems (RecSys) that went on to serve millions of customers. For each chapter, I will walk through case studies and share my experience designing RecSys. This is part of my Modern Visual RecSys series; feel free to check out the rest of the series at the end of the article. We begin with a case study of Spotify to understand how RecSys works and introduce several key concepts, including a modern approach called convolutional neural networks (CNN), applied to music. Let us take a look at my personalized music recommendations from Spotify.


How We Improved Data Discovery for Data Scientists at Spotify

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Not only does this provide useful information to users in the moment, but it has also helped raise awareness and increase the adoption of Lexikon. Since launching the Lexikon Slack Bot, we've seen a sustained 25% increase in the number of Lexikon links shared on Slack per week. You just listened to a track by a new artist on your Discover Weekly and you're hooked. You want to hear more and learn about the artist. So, you go to the artist page on Spotify where you can check out the most popular tracks across different albums, read an artist bio, check out playlists where people tend to discover the artist, and explore similar artists.


This Music-Playing Robot Kills 99.99% of Bacteria, Germs, and Mites

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In life, bacteria are everywhere. Even on the cleanest sheets and pillowcases, bacteria are there. You can't see them with the naked eye but that does not mean they can not harm you. They cause you to get colds, viral infections, and put your life at risk. How can one get rid of these bacteria?


How Machine Learning Is Changing the World

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Most of our decisions on social media are impacted by ML. From feeds that we see on the timeline to notifications that we receive from the social media apps, everything is curated by ML. While we travel, work, live life our decisions are examined by machine learning to provide us with a better experience. ML takes all the past behavior, web searches, interaction and everything else that we do when we are on these websites and tailors the experience for us. It helps enhance our web surfing into a personalized one.


KR38R - Hip Hop, Powered By AI

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Introducing one of the most advanced music AI engines ever created. CREATE BEATS Every MixPack features 4 tracks, 12 loops, and 100 combinations. ADD VIDEOS Shoot live or add clips from your library. The KR38R camera lets you combine up to 3 clips - perfect for rappers to multi-track vocals or dancers to shoot routines from multiple angles. COMING 2020 Coming soon to the KR38R app are features and functionality guaranteed to change the game, including: AI-powered tools for producers, video and audio filters, audio-reactive video fx, and more.


Comparing Western and Chinese classical music using deep learning algorithms

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Deep learning techniques are proving to be extremely useful for analyzing all kinds of data, ranging from images to text, online posts and audio recordings. These techniques are designed to identify patterns in large datasets, separate items in different categories and make predictions far quicker than humans. In a recent study, researchers at Simon Fraser University, Academia Sinica and Dartmouth College have applied deep learning techniques to identify similarities and differences between Chinese and Western classical music. Their paper, pre-published on arXiv, presents a comparative analysis of music recordings using sound event detection (SED) and soundscape emotion recognition (SER) models. "We have listened to both Chinese and Western classical music," Jianyu Fan, one of the researchers who carried out the study, told TechXplore.


For Your Ears Only: Personalizing Spotify Home with Machine Learning

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This article is based on the keynote given by Tony Jebara at TensorFlow World in Santa Clara, California, October 2019. You can watch the presentation here. Machine learning is at the heart of everything we do at Spotify. Especially on Spotify Home, where it enables us to personalize the user experience and provide billions of fans the opportunity to enjoy and be inspired by the artists on our platform. This is what makes Spotify unique.


5 Ways AI Is Impacting Our Everyday Lives

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Some people associate AI with science fiction robots, but in reality, the technology streamlines and improves many day-to-day processes, allowing us to accomplish more in less time. Today, AI plays a role in many aspects of our daily lives, from commuting to shopping to browsing the web. AI software development is a massive market, and the technology is improving all the time. Experts predict that the AI social media market will grow from $633 million in 2018 to $2.1 billion in 2023. As both artificial intelligence and social media become more prevalent in people's lives, the intersection of the two will also become more popular.


Artificial Intelligence Piano - My Hackweek Project - SUSE Communities

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This article has been contributed by Lin Ma, Software Engineer and KVM Virtualization Specialist at SUSE. With this article, I would like to introduce you to my SUSE Hackweek 19 project. If you worked on similar projects or topics, or if you would like to exchange experiences, please feel free to reach out to me. As a Do-it-Yourself (DIY) enthusiast, I decided to have some fun during Hackweek with music and machine learning based on SUSE Linux Enterprise Server. Or, if you will, you could also think of my project as an Internet of Things (IoT) attempt based on SUSE products.


The One Minute AI #17 - Computer Vision

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Welcome to a new series of short articles I am presenting about Artificial Intelligence specifically in the Azure AI stack. The objective is that you will learn about an Azure based AI service in no more than one minute and thus quickly get familiar with the entire stack over a short period of time. These are going short, easily digestible articles so let's get started!