[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117742-en":3,"doc-seo-117742-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117742,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","A Distance Model for Rhythms","Long-term dependencies in time series remain difficult for conventional machine learning approaches, particularly when the data are musical. This paper presents a generative model for rhythm patterns built on the probability distributions of distances between fixed-length subsequences. A specific implementation using Hamming distance on a simple rhythm representation is developed and evaluated. Experiments show the proposed distance-based model consistently improves conditional prediction accuracy over a standard Hidden Markov Model on two different music databases.","A Distance Model for Rhythms  \nJean-Francois Paiement [paiement@idiap.ch](paiement@idiap.ch)  \nYves Grandvalet [Yves.Grandvalet@utc.fr](Yves.Grandvalet@utc.fr)  \nIDIAP Research Institute, Case Postale 592, CH-1920 Martigny, Switzerland  \nSamy Bengio [bengio@google.com](bengio@google.com)  \nGoogle, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA  \nDouglas Eck [douglas.eck@umontreal.ca](douglas.eck@umontreal.ca)  \n[Universit](Universit) 􀀓e de Montr􀀓eal, Department of Computer Science, CP 6128, Succ. Centre-Ville, Montr􀀓eal, Qu􀀓ebec H3C 3J7, Canada  \nAbstract  \nModeling long-term dependencies in time series has proved very di􀀎cult to achieve with traditional machine learning methods. This problem occurs when considering music data.  \nIn this paper, we introduce a model for rhythms based on the distributions of distances between subsequences. A speci􀀌c implementation of the model when considering Hamming distances over a simple rhythm representation is described. The proposed model consistently outperforms a standard Hidden Markov Model in terms of conditional prediction accuracy on two di􀀋erent music databases.  \n1. Introduction  \nReliable models for music would be useful in a broad range of applications, from contextual music generation to on-line music recommendation and retrieval. However, modeling music involves capturing long-term dependencies in time series, which has proved very dif-􀀌cult to achieve with traditional statistical methods. Note that the problem of long-term dependencies isnot limited to music, nor to one particular probabilistic model (Bengio et al., 1994) .  \nMusic is characterized by strong hierarchical dependencies determined in large part by meter, the sense of strong and weak beats that arises from the interaction among hierarchical levels of sequences having  \nAppearing in Proceedings of the 25 th International Conference on Machine Learning, Helsinki, Finland, 2008 . Copyright 2008 by the author(s)/owner(s) .  \nnested periodic components. Such a hierarchy is implied in western music notation, where di􀀋erent levels are indicated by kinds of notes (whole notes, half notes, quarter notes, etc.) and where bars establish measures of an equal number of beats. Meter and rhythm provide a framework for developing musical melody. For example, a long melody is often composed by repeating with variation shorter sequences that 􀀌t into the metrical hierarchy (e.g. sequences of 4, 8 or 16 measures) . It is well know in music theory that distance patterns are more important than the actual choice of notes in order to create coherent music (Handel, 1993) . In this work, distance patterns refer to distances between subsequences of equal length in particular positions. For instance, measure 1 may be always similar to measure 5 in a particular musical genre. In fact, even random music can sound structured and melodic if it is built by repeating random subsequences with slight variation.  \nMany algorithms have been proposed for audio beat tracking (Dixon, 2007; Scheirer, 1998) . Probabilistic models have also been proposed for tempo tracking and inference of rhythmic structure in musical audio (Whiteley et al., 2007; Cemgil & Kappen, 2002) . The goal of these models is to align rhythm events with the metrical structure. However, simple Markovian assumptions are used to model the transitions between rhythms themselves. Hence, these models do not take into account long-term dependencies. A few generative models have already been proposed for music in general (Pachet, 2003; Dubnov et al., 2003) . While these models generate impressive musical results, we are not aware of quantitative comparisons between models of music with machine learning standards, as it is done in Section 3 in terms of out-of-sample prediction accuracy. In this paper, we focus on modeling rhyth-  \nmic sequences, ignoring for the moment other aspects of music such as pitch, timbre and dynamics. However, by capturing aspects of global temporal structur","cbCaiuG5DzlDJD8N","https://ap.wps.com/l/cbCaiuG5DzlDJD8N","pdf",649705,1,"English","en",105,"# Abstract\n# Introduction\n# Distance Model\n## Motivation","[{\"question\":\"What challenge does the paper address in time-series modeling?\",\"answer\":\"It targets the difficulty of capturing long-term dependencies in time series, which is especially problematic for music data.\"},{\"question\":\"How does the proposed model represent rhythms?\",\"answer\":\"It models rhythms using the distributions of distances between subsequences of equal length in specific positions.\"},{\"question\":\"What comparison is used to evaluate the model’s effectiveness?\",\"answer\":\"The distance model is compared against a standard Hidden Markov Model using conditional prediction accuracy on two music databases.\"}]","A Distance Model for Rhythms | PDF",1785679305,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"a-distance-model-for-rhythms","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/a-distance-model-for-rhythms/117742/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What challenge does the paper address in time-series modeling?","Question",{"text":74,"@type":75},"It targets the difficulty of capturing long-term dependencies in time series, which is especially problematic for music data.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed model represent rhythms?",{"text":79,"@type":75},"It models rhythms using the distributions of distances between subsequences of equal length in specific positions.",{"name":81,"@type":72,"acceptedAnswer":82},"What comparison is used to evaluate the model’s effectiveness?",{"text":83,"@type":75},"The distance model is compared against a standard Hidden Markov Model using conditional prediction accuracy on two music databases.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]