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Machine Learning enhances User Experience for Music

When creativity meets technology, you get incredible outcomes. And that is what the music streaming industry is investing in these days to improve user experience amidst brutal competition. They push new boundaries with technology and diversify the music genre so that everyone can appreciate it.

The era of personalized music with machine learning

In the latest news, the music discovery process is getting personalized results with revolutionary machine learning. Nowadays, almost every big name of the industry is leveraging AI to create better and more personalized music lists.

So, you should not get surprised if the suggested music from Spotify, Pandora and Apple Music seems exactly what you want to hear. All these music-streaming providers implement complex algorithms to pick subtle cues and create personalized music list for you.

• Pandora combines the same technology with data analytics to make suggested playlists for listeners. The algorithms used by Pandora evaluate the songs or artists selected by a user. With that, it creates a playlist that has similar attributes, matching the personal preferences of that user.

 

• Spotify is probably the most enthusiastic player when it comes to using algorithm technology in music streaming. The company uses a collaborative filtering approach. The algorithms collect music streaming data from multiple users and compares it together. This comparison is conducted with Echo Nest, which is considered best in this technology for music search. Apart from collaborative filtering, Spotify also includes NLP and audio models in its method of providing personalized music.

How Machine learning is evolving music streaming personalization

As mentioned earlier, music-streaming companies are using a variety of AI technologies to make song discovery advanced and personalized.

Here are three major technologies revolutionizing the music-streaming industry.

1. NLP or Natural Language Processing

NLP enables algorithms to understand human language. APIs are used for sentiment analysis, which harnesses the meaning behind spoken and written words. The model of NLP allows music streaming providers to collect data from a variety of resources all over the internet. Algorithms collect data from articles, news, blogs and other resources available on the internet. Using the written text regarding a music, the machines understand the characteristics and provide them with the right playlists.

2. Collaborative filtering

Collaborative filtering is a comparative study of the users’ music listening behavior. The technology helps in understanding the popularity and characteristics of songs. Algorithms collect data from a wide range of users. These datasets include information regarding stream counts, saved tracks, page visits and many others.

By incorporating all kinds of streaming data together, algorithms create a personalized list of tracks for the listeners.

3. AI audio models

Companies like Spotify understand that NLP and collaborative filtering cannot offer justice for new songs. That is why they use another form of AI-Integrated audio model. This technology works just like the face recognition technology. However, the algorithms inspect the audio models instead of pixels. With raw audio evaluation, companies provide new songs to the users in their playlist.

Thus, it would not be wrong to say that machine learning has found a strong place in the large ecosystem of music discovery. With proven phenomenal outcomes, the justification of marrying AI with music does make total business sense!