1 / 15

Similarity Measures for Rhythmic Sequences

Joao Martins Marcelo Gimenes J ô natas Manzolli Adolfo Maia Jr. Future Music Lab – University of Plymouth NICS – UNICAMP. Similarity Measures for Rhythmic Sequences. INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS. Outline ¦ Scope ¦ Other Measures. INTRODUCTION SCV EXAMPLES

gafna
Download Presentation

Similarity Measures for Rhythmic Sequences

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. Joao MartinsMarcelo GimenesJônatas Manzolli Adolfo Maia Jr. Future Music Lab – University of PlymouthNICS – UNICAMP Similarity Measures for Rhythmic Sequences 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  2. INTRODUCTIONSCVEXAMPLESAPPLICATIONSCONCLUSIONS Outline ¦ Scope¦ Other Measures • INTRODUCTION • SCV • EXAMPLES • APPLICATIONS • CONCLUSIONS 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  3. INTRODUCTIONSCVEXAMPLESAPPLICATIONSCONCLUSIONS Outline¦ Scope ¦ Other Measures • Similarity measures are fundamental in music information retrieval and play one of the most important roles in Artificial Intelligence towards the establishment of fitness functions. • The aim is to create a similarity measure for rhythmic sequences that can capture patterns in several hierarchical levels, spanning from a small rhythmic phrase to longer structures. 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  4. INTRODUCTIONSCVEXAMPLESAPPLICATIONSCONCLUSIONS Outline¦ Scope¦ Other Measures • Euclidean distance • Levenshtein distance • Mongeau and Sankoff (1990) 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  5. INTRODUCTIONSCVEXAMPLESAPPLICATIONSCONCLUSIONS Representation¦ Similarity Coefficient Vector ¦ Model • Representation of rhythmic sequences previously quantized discarding expressive timing info • Shmulevich, I. and Povel, D. (2000) 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  6. INTRODUCTIONSCVEXAMPLESAPPLICATIONSCONCLUSIONS Representation¦ Similarity Coefficient Vector ¦ Model • Similarity Coefficient Vector (SCV) • This vector is a measure of similarity between all the subsequences • It is built counting the sparsity of a distances matrix for a given k-level 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  7. INTRODUCTIONSCVEXAMPLESAPPLICATIONSCONCLUSIONS Representation¦ Similarity Coefficient Vector ¦ Model • Diagram 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  8. INTRODUCTIONMODELExamplesAPPLICATIONSCONCLUSIONS Building the Matrix ¦ ≠ Length¦ Finding the most similar • Example on how the algorithm builds the 3rd level matrix for two sequences of different lengths. 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  9. INTRODUCTIONMODELExamplesAPPLICATIONSCONCLUSIONS Building the Matrix ¦ ≠ Length¦ Finding the most similar • This is an example of the comparison between the sequences • V = 1 0 1 1 • W = 1 0 1 1 0 1 • The first sequence is completely included in the second, therefore we can find a positive value in the last level of the SCV • The sum of all coefficients of the SCV is 1.625 which can be seen as a single value expressing similarity between the sequences 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  10. INTRODUCTIONMODELExamplesAPPLICATIONSCONCLUSIONS = Length ¦ ≠ Length¦ Finding the most similar • Matlab application to explore the similarities in the rhythmic space • Gray code 0 0 0 0 1 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 1 0 0 1 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  11. INTRODUCTIONMODELEXAMPLESAPPLICATIONSCONCLUSIONS Musicology ¦ NetRhythms ¦ RGem ¦ Others • Computational musicology is broadly defined as the study of Music by means of computer modelling and simulation. • Complimentary approach to traditional musicology • What theories of music evolutionary origins make sense? • How do learning and evolved components interact to shape the musical culture that develops over time? 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  12. INTRODUCTIONMODELEXAMPLESAPPLICATIONSCONCLUSIONS Musicology ¦ NetRhythms ¦ RGem ¦ Others SARDNET (Sequential Activation Retention and Decay Network) is an extended Kohonen self-organising feature map. This network was developed to study sequences and organization of phonemes in the context of language (James and Miikkulainen (1995) • The input sequence • Each element of V is a vector in which the correspond to small rhythmic group with sampled events and amplitude • The network weights • The weight vectors W correspond to the internal representation of the agents Comparison using the SCV determines the winning node of the network 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  13. INTRODUCTIONMODELEXAMPLESAPPLICATIONSCONCLUSIONS Musicology ¦ NetRhythms ¦ RGeme ¦ Others Simulation dFL: date of first listening dLL: date of last listening nL: number of listenings W: weight Style Matrix 1 Style Matrix 2 time = 1 Every time a new music is listened to, new memes are included in the Style Matrix and the weights of all the memes are updated according to the similarity measure . time = 2 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  14. INTRODUCTIONMODELEXAMPLESAPPLICATIONSCONCLUSIONS Musicology ¦ NetRhythms ¦ RGem ¦ Others • Composition • Pedagogy 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

  15. INTRODUCTIONMODELEXAMPLESAPPLICATIONSCONCLUSIONS Contributions ¦ Future work ¦ Acknowledgements • Contributions • This work contributes with a measure of similarity between sequences, exploring all hierarchical levels and keeping the information about the lower levels. • Future Work • Future developments involve the comparison between the SCV and other similarity measurements and how can we relate this measurement with human perception • Acknowledgements • The authors would like to acknowledge the financial support of the Lerverhulme Trust, São Paulo State Research Foundation (FAPESP) and CAPES (Brazil) 10º Simpósio Brasileiro de Computação Musical (SBCM2005)

More Related