Btp 1st

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Btp 1st

  1. 1. Music Recommandation System Group - G6 Advisor – Dr. Vikram Pudi
  2. 2. Music != Movies and books • CF algorithms are generally suffers from cold- start problem, novelty and ignore content of items. • Tracking user’s preference is mostly done implicitly, via their listening habits instead of asking users to explicitly rate the item • Any user can consume the item several times, even repeatedly and continuously. Mostly music labeling should be done by music experts. • Another big difference is context of the music, like people prefer hard-rock in the morning, classical piano while working, and cool jazz while having dinner. It should also handle these contextual differences
  3. 3. Audio Feature Extraction • Audio data is the time series where y-axis is current amplitude and x-axis is time.
  4. 4. Continue .. • Audio waveform is broken into short frames. (1024 samples at 22050Hz). • Collect Frame-level features and get mean/variance for each frames • Discrete short term Fourier transformation • Real Cepstral Coefficients • Mel Frequency Cepstral Coefficients • Zero crossing rate • Septral centroid, Rolloff, flux and LPC • Rhythmic and Harmony Beat features .. • We got a 68 floating point vector called feature vector for a audio file. • Computationally expensive
  5. 5. 2. Automatic Playlist Generation using a song seed • Aim:- given a song suggest most similar song in the library and make a mood-based playlist • Implementation- • Seed song s0 , F0 = {p1 , p2 , …………., pn} • Find song s { S- s0} where difference between feature vector is minimum. • Repeat the steps to generate whole playlist up to certain tolerance. • Two mode:- seed song, last recommended song • Note that there is no user involved.
  6. 6. Problem in User scenario • Previous was simplest case of recommendation problem. • No user preferences are involved. • Where is user profiles , musical taste, feedback loops, ratings, listening habits ? • Result depends upon seed song, all the time • Not ideal situation when user has various list of songs already in his playlist. • Simple Averaging can’t be the right solution
  7. 7. 3. Top-N recommendation • Solution: Clustering with dynamic K-mean • Cluster the song using kmean algorithm based on their feature vectors. • But we don’t know the initial K ? Solution • Fix a Radius R at which a genre is usually clustered. If user like 3 genres, finally 3 or more clusters will be the outcome • Algorithm starts with k=1, if radius > R, increase K by 1 (=2) and recalculate until all cluster’s radius <= R. • Then find score of each music and select top-N items, N is given by user.
  8. 8. Ranking and scoring items • Calculate score of each music as • • Score(m,c) = 1 * ClusterData(c) -------------------------- Dist( Vc , Vm ) * AllData • score(m,c) = score of music item with cluster c, • ClusterData(c) = number of music instances in cluster c, • Dist(v,u) = Euclidian distance between music item and cluster centroid. Hence more closer to the centroid, more chances of getting recommended. i.e. higher score. • Alldata = number of pieces in users’ playlist. • Sum the score for each cluster. • Sort down the score and recommend top-N items.
  9. 9. Stats • Dataset = 'A benchmark for automatic genre classification" • +----------+--------+ • | tag | count(*) | • +-------------+---------+ • | alternative | 145 | • | blues | 120 | • | electronic | 113 | • | folkcountry | 222 | • | funksoulrnb | 47 | • | jazz | 319 | • | pop | 116 | • | raphiphop | 300 | • | rock | 504 | • +-------------+----------+
  10. 10. Limitation • No user feedback loops • Determining R is a problem, this approach fails in case of numerous genres. • Content based recommendation are less accurate . • Hard –time of mapping user preferences into music domain. • More feature will increase the result but clustering is a expensive with big feature vectors. • Scaling problem, not efficient.
  11. 11. Future work • UI front-end and Interfaces • Improving recommendation algorithm defined in literature. • Gathering implicit feedback and tracking user- profiles. • Playlists according to artists, user-profile as a seed. • Recommendation from a song-set. • Mining web for new music information and other attributes of songs. Mp3 blogs, web services API last.fm, mystrands, pandora etc.
  12. 12. References • Adomavicius, G. and Tuzhilin A.(2005), “Towards the next generation of recommender system: A survey and state-of-art and possible extensions” • Aucouturier, J-J. and Packet, F. (2002), “Music similarity measures: What’s the use?” • P. Cano, M. Kopperbergerger, N. Wack, “Content based music audio recommendation” • B. Logan, “Music recommendation from song-sets” • G. Tzanetakis, P. Cook, “Musical genre classification of audio signals” • J.H. Ban, K.M. Kim, K.S. Park, “Quick audio retrieval using multiple feature vector” • Canno, P., Koppenberger, M., and Wack, N. (2005), “An industrial-strength content based music recommendation system ”

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