Music Recommendation System

Transcription

1 Music Recommendation System Using Genetic Algorithm Date > May 26, 2009 Kim Presenter > Hyun-Tae Kim

2 Contents 1. Introduction 2. Related Work 3. Music Recommendation System - A s MUSIC 4. Conclusion

3 Introduction

4 Recommendation System?! Nowadays, there are a large amount of various data on the Internet or the computer. Therefore, many problem that users (End users) meet has raised. This item is good or bad?? Many websites provide recommendation system in order to increase user s convenience. (e,g. Amazon.com / Genius (itunes) / Last.fm)

5 Recommendation System?! (cont ) Recommendation Systems forms a specific type of information filtering technique that attempts to present information items. (like movies, music, books, news, images, etc.) Typically, a recommendation system compares the user s profile to some reference characteristics, and seeks to predict the rating. These characteristics may be from the information item (the content-based approach) the user s social environment (the collaborative filtering approach)

6 Main Approaches of R.S. Data Data Data User Profiles User Profiles User Profiles system Compute Similarities system Compute Similarities User Profile Content-based approach Data Data Data Data Collaborative approach

7 Music Recommendation System?! There are many internet-based music site which provide listening on online or download to users. (e,g. Melon, Dosirak, Bugs Music, etc.) So, some music recommendation system (MRS) are already existed. Most of those system use contentsbased approach or collaborative approach. I have applied genetic algorithm to MRS. Main goal of my system is that the system can recommend appropriate songs which is satisfied with the user s feeling or preference.

8 Related Work

9 Example of MRS - 1 (Genius / Apple Corp.) Genius is a recommender system in the itunes media player computer software that uses collaborative approach to generate music recommendations. It uses the integrated rating system built into itunes. This allows for better recommendations because of a lager data set. When user click on the arrows to view the music information on the itunes Store, this software provides links on the side to other related music found in the Store.

10 Example of MRS - 2 (Lasf.fm / Web site) It uses a music recoomender system called as Audioscrobbler. Last.fm builds a detailed profile of each user s musical tastes by recording details of all the songs the user listens to, either on the streamed radio stations, the user s computer or many portable music devices. This information is transferred to Last.fm s DB ( scrobbled ) via a plugin installed into the user s devices. Last.fm offers numerous social networking features and recommend and play artists similar to the user s preferences.

11 Example of MRS - 3 (Car audio system / Pioneer Corp.) They have developed a MRS that can learn a variety of user s preferences. The MRS that they have developed is able to extract some musical feature that they call Music Feature automatically from a CD alone when inserted into the CD-ROM drive, without any prior information related to its pieces of music. They are classified by five different types of mood (e,g. Bright, Exciting, Quiet, Sad and Healing ) which will be configured with extracted musical features.

12 Feature Extraction from music. Clam [ is a software framework for research and application development on the audio and music domain. It provides means to perform complex audio signal analysis, transformations and synthesis. Marsyas [ is a software framework for rapid prototyping and experimentation with audio analysis and synthesis with specific emphasis to music signals and Music Information Retrieval.

13 Music Recommendation System - A s MUSIC

14 Basic Idea & Assumption. Apply genetic algorithm to music recommendation system. The system can detect and recommend appropriate songs which is suitable for user s musical preference. And, the system requires pre-processing which is feature (i,e. tempo, chord, pitch, etc.) extraction from music. It based on shuffle operation. (i.e, play song randomly) At first time, the system recommends songs randomly and user can judges song s preference by controlling their devices or program. Just click the next song button.

15 System Structure. Song Information Play song & Check user s behavior Evaluate current playlist & Make new playlist

16 Procedure - (ALL). musical feature extraction Pre-processing play & evaluate fitness Playlist 1 Playlist 2 Update List Update List New Playlist 1 done Playlist 1 done Playlist 2 New Playlist 2 evaluate & Make List evaluate & Make List

17 Procedure (Pre-Processing). musical feature extraction Pre-processing 10 digit 100 Music data (*.mp3) Using CLAM to extract musical Factors Num Tempo Pitch Octave Root Mode digit Parsing XML Tempo Note Chord

18 Procedure - (Play & Evaluate fitness). play & evaluate fitness Playlist 1 Each Playlist contains 10 songs. Evaluate Fitness value. done Playlist 1 1. check the time that user click next song button. 2. the time divid by song s playing time

19 Procedure - (Evaluate & Make List). play & evaluate fitness Playlist 1 Playlist 1 play is done. Select the high rated Songs. (truncation selection) New Playlist 1 done Playlist evaluate & Make List

20 Procedure - (Evaluate & Make List) cont. play & evaluate fitness Playlist 1 Crossover Mix musical feature. (1-point crossover) New Playlist 1 done Playlist 1 evaluate & Make List Num Tempo Pitch Octave Root Mode Add new song which is similar to new individual. Parent s tempo Candidate items which be new generation

21 Procedure - (Update List). Update List play & evaluate fitness Playlist 1 Update Playlist If number of playlist is less than 10, add some songs to playlists randomly. New Playlist 1 done Playlist 1 evaluate & Make List

22 Test environment. Programming Language : C++ OS : Mac OS X Compiler : g Use 100 Korean Pops Musical feature extraction tool : clam Length of Playlist : 20

23 Test result (The Nuts) - ( ) (Feat. of ) 1 5 KCM Tell Me Why (Feat. ) Extreme (Feat. ) Wonderful 5 10 (YB) (Someday) V.O.S ( ) (Feat. ) (Brand New Day) (Feat. of ) ", " - (Feat. )

24 Conclusion

25 Conclusion. On this project, I have developed recommendation system using genetic algorithm. I used only 3 elements of music. I got good result than I expected. However, it still has some probability to occur error or bugs. Because, this system is my on-going project that started 1 month ago. So, some works will continue such as more debugging, visualization, implementation another platform or programming language.

26 Q & A

27 Thank YoU for your attention!!