TY - CONF T1 - Real-time Music Tracking using Multiple Performances as a Reference T2 - Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) Y1 - 2015 A1 - Andreas Arzt A1 - Widmer, G. JF - Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) ER - TY - CONF T1 - Refined Spectral Template Models for Score Following T2 - Proceedings of the Sound and Music Computing Conference (SMC) Y1 - 2013 A1 - Korzeniowski, Filip A1 - Widmer, Gerhard AB - Score followers often use spectral templates for notes and chords to estimate the similarity between positions in the score and the incoming audio stream. Here, we propose two methods on different modelling levels to improve the quality of these templates, and subsequently the quality of the alignment. The first method focuses on creating more informed tem- plates for individual notes. This is achieved by estimating the template based on synthesised sounds rather than generic Gaussian mixtures, as used in current state-of-the-art systems. The second method introduces an advanced approach to aggregate individual note templates into spectral templates representing a specific score position. In contrast to score chordification, the common procedure used by score fol- lowers to deal with polyphonic scores, we use weighting functions to weight notes, observing their temporal relationships. We evaluate both methods against a dataset of classical piano music to show their positive impact on the alignment quality. JF - Proceedings of the Sound and Music Computing Conference (SMC) CY - Stockholm, Sweden ER - TY - CONF T1 - repoVizz: a Framework for Remote Storage, Browsing, Annotation, and Exchange of Multi-modal Data T2 - ACM Multimedia 2013 Y1 - 2013 A1 - Mayor, Oscar A1 - Llimona, Quim A1 - Marchini, Marco A1 - Papiotis, Panos A1 - Maestre, Esteban KW - exchange KW - HTML5 KW - multimodal KW - remote KW - repository AB -
In this technical demo we present repoVizz (http://repovizz.upf.edu), an integrated online system capable of structural formatting and remote storage, browsing, exchange, annotation, and visualization of synchronous multi-modal, time-aligned data. Motivated by a growing need for data-driven collaborative research, repoVizz aims to resolve commonly encountered diculties in sharing or browsing large collections of multi-modal data. At its current state, repoVizz is designed to hold time-aligned streams of heterogeneous data: audio, video, motion capture, physiological signals, extracted descriptors, annotations, et cetera. Most popular formats for audio and video are supported, while Broadcast WAVE or CSV formats are adopted for streams other than audio or video (e.g., motion capture or physiological signals). The data itself is struc tured via customized XML les, allowing the user to (re-)organize multi-modal data in any hierarchical manner, as the XML structure only holds metadata and pointers to data files. Datasets are stored in an online database, allowing the user to interact with the data remotely through a powerful HTML5 visual interface accessible from any standard web browser; this feature can be considered a key aspect of repoVizz since data can be explored, annotated, or visualized from any location or device. Data exchange and upload/download is made easy and secure via a number of data conversion tools and a user/permission management system.
JF - ACM Multimedia 2013 PB - ACM Multimedia CY - Barcelona UR - http://acmmm13.org/ ER - TY - Generic T1 - Rhytmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio T2 - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) Y1 - 2013 A1 - Krebs, Florian A1 - Sebastian Böck A1 - Widmer, Gerhard AB -

Rhythmic patterns are an important structural element in music. This paper investigates the use of rhythmic pattern modeling to infer metrical structure in musical audio recordings. We present a Hidden Markov Model (HMM) based system that simultaneously extracts beats, downbeats, tempo, meter, and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al, which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. For learning rhythmic patterns and evaluating beat and downbeat tracking, 697 ballroom dance pieces were annotated with beat and measure information. The results showed that explicitly modeling rhythmic patterns of dance styles drastically reduces octave errors (detection of half or double tempo) and substantially improves downbeat tracking.

JF - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) ER -