The final step is the recommendation system that would work on the property of k- nearest neighbors to determine the nearest match of user taste with the recommendation data. It will analyze the sentiments of the song pertaining to a particular user and recommend the nearest matches to that user from the dataset. The recommendations will be send through an API to the respective user at the respective platform.
Our ML tools and Audio Lab, optimizes playlists and recomments various streaming platforms that can be used to enhance your customer experience.
Music Machine learning
The ultimate interactive music creativity with deep learning tools to enhance music distribution efficiency.
We are able to match user data input to publish recommendations for music styles, genres, bpm tempo,
artist profiles, and playlists.
Feature Extraction and Sentiment Analysis
The audio will be analyzed and the Mel-frequency cepstral coefficients (MFCCs) features will be extracted from it. Then we perform sentiment analysis of the song to determine the characteristics like mood such as passionate, rousing, tempo, style, etc, along with the texture and genre of the song.
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