July / º Simpósio Brasileiro de Computação Musical (SBCM2005) Joao Martins Marcelo Gimenes Jônatas Manzolli Adolfo Maia Jr. Future Music Lab – University of Plymouth NICS – UNICAMP Similarity Measures for Rhythmic Sequences

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS Outline ¦ Scope ¦ Other Measures

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) 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. INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS Outline ¦ Scope ¦ Other Measures

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) Euclidean distance Levenshtein distance Mongeau and Sankoff (1990) INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS Outline ¦ Scope ¦ Other Measures

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS Representation ¦ Similarity Coefficient Vector ¦ Model Representation of rhythmic sequences previously quantized discarding expressive timing info Shmulevich, I. and Povel, D. (2000)

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS 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

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION SCV EXAMPLES APPLICATIONS CONCLUSIONS Representation ¦ Similarity Coefficient Vector ¦ Model Diagram

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION MODEL Examples APPLICATIONS CONCLUSIONS Building the Matrix ¦ ≠ Length ¦ Finding the most similar Example on how the algorithm builds the 3 rd level matrix for two sequences of different lengths.

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) This is an example of the comparison between the sequences V = W = 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 which can be seen as a single value expressing similarity between the sequences INTRODUCTION MODEL Examples APPLICATIONS CONCLUSIONS Building the Matrix ¦ ≠ Length ¦ Finding the most similar

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION MODEL Examples APPLICATIONS CONCLUSIONS = Length ¦ ≠ Length ¦ Finding the most similar Gray code Matlab application to explore the similarities in the rhythmic space Matlab application

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION MODEL EXAMPLES APPLICATIONS CONCLUSIONS 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?

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION MODEL EXAMPLES APPLICATIONS CONCLUSIONS Musicology ¦ NetRhythms ¦ RGem ¦ Others 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 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) Comparison using the SCV determines the winning node of the network

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION MODEL EXAMPLES APPLICATIONS CONCLUSIONS Musicology ¦ NetRhythms ¦ RGeme ¦ Others Style Matrix 1Style Matrix 2 time = 1 Simulation time = 2 dFL: date of first listening dLL: date of last listening nL: number of listenings W: weight 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.

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) INTRODUCTION MODEL EXAMPLES APPLICATIONS CONCLUSIONS Musicology ¦ NetRhythms ¦ RGem ¦ Others Composition Pedagogy

July / º Simpósio Brasileiro de Computação Musical (SBCM2005) 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) INTRODUCTION MODEL EXAMPLES APPLICATIONS CONCLUSIONSContributions ¦ Future work ¦ Acknowledgements