1 / 10

Combining Musical and Cultural Features for Intelligent Style Detection

This study proposes an automatic style detection system that combines both the acoustic content of audio and community metadata. The system utilizes a dataset of five styles, each with five different artists. The classification is based on audio-based and community metadata-based approaches. The combined classification approach shows promising results for accurate style detection. Future work includes the development of a "culture ratio" to enhance recommendation engines.

williamfranklin
Download Presentation

Combining Musical and Cultural Features for Intelligent Style Detection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Combining Musical and Cultural Features for Intelligent Style Detection Brian Whitman Paris Smaragdis MIT Media Lab

  2. Background • Music classification by style • A “human” concept; hard to model. • Defines subclasses of genres. • Can be utilized by recommendation engine for high-confidence results. ISE599 - by Frances Kao

  3. Approach • An automatic style detection system that operate on both of • acoustic content of the audio • community metadata: a vector space of descriptive textual terms crawled from the web • Dataset: 5 styles, each with 5 different artists ISE599 - by Frances Kao

  4. Audio-based Classification • Form each song into some presentation • Train a neural network to classify a song ISE599 - by Frances Kao

  5. Audio-based Classification – Result ISE599 - by Frances Kao

  6. Community Metadata-based Classification (1) • Cultural feature • Each artist is associated with terms which appear on the same web document as the artists’ name. • Each term has a score calculated in terms of position and frequency of occurrence. ISE599 - by Frances Kao

  7. Community Metadata-based Classification (2) • Similarity • For every 2 artists, calculate an overlap weight, which is the summation of every shared term. • Form a similarity matrix to predict the style of each artist ISE599 - by Frances Kao

  8. Community Metadata-based Classification - Result ISE599 - by Frances Kao

  9. Combined Classification ISE599 - by Frances Kao

  10. Conclusion & Future Work • Combined classification can overcome all the problems • Future development can use a “culture ratio” to alert recommendation engines to use which classification method. ISE599 - by Frances Kao

More Related