Oscar Mayor

ESSENTIA: an open-source library for sound and music analysis

We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. The library is cross-platform and currently supports Linux, Mac OS X, and Windows systems. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications.

repoVizz: a framework for remote storage, browsing, annotation, and exchange of multi-modal data

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 difficulties 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 structured via customized XML files, 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.

Enriched Multimodal Representations of Music Performances: Online Access and Visualization

The authors provide a first-person outlook on the technical challenges involved in the recording, analysis, archiving, and cloud-based interchange of multimodal string quartet performance data as part of a collaborative research project on ensemble music making. To facilitate the sharing of their own collection of multimodal recordings and extracted descriptors and annotations, they developed a hosting platform through which multimodal data (audio, video, motion capture, and derived signals) can be stored, visualized, annotated, and selectively retrieved via a web interface and a dedicated API. This article offers a twofold contribution: the authors open their collection of enriched multimodal recordings, the Quartet dataset, to the community, and they introduce and enable access to their multimodal data exchange platform and web application, the Repovizz system. This article is part of a special issue on multimedia technologies for enriched music.

KaleiVoiceKids: interactive real-time voice transformation for children

In this paper we describe the adaptation of an existing Real-time voice transformation exhibit to the special case of children as the interacting subjects. Many factors have been taken into consideration to adapt the body interaction design, the visual feedback given to the user and the core technology itself to fulfill the requirements of children. The paper includes a description of this installation that is being used daily by hundreds of children in a permanent museum exhibition.

A personality-based adaptive system for visualizing classical music performances

To enhance the experience of listening to classical orchestra music, either in the concert hall or at home, we present a personalized system that integrates three visualization/interaction concepts: Score Follower (points to the current position in the score), Orchestra Layout (illustrates instruments that are currently playing and their dynamics), and Structure Visualization (visualizes structural elements such as themes or motifs). Motivated by previous literature that found evidence for connections between personality and music consumption and preference, we first assessed in a user study to which extent personality traits and music visualization preferences correlate. Measuring preference via pragmatic quality and personality traits according to the Big Five Inventory (BFI) questionnaire, we found substantial interconnections between them. These translate into rules relating certain personality traits (e.g., extraversion or agreeableness) to preference rankings of the visualizations. In the proposed personality-based system, users are grouped into four clusters according to their answers to the most significant personality questions determined in the study. The order of the visualizations for a given user is adapted with respect to the ranking preferred by other users in the same cluster. Evaluation of the system was carried out by a second user study that showed a significantly higher normalized discounted cumulative gain (NDCG) for the personalized system in comparison to a system with randomized order of the visualizations.

Enhancing Music Learning with Smart Technologies

Learning to play a musical instrument is a difficult task, requiring the development of sophisticated skills. Nowadays, such a learning process is mostly based on the master-apprentice model. Technologies are rarely employed and are usually restricted to audio and video recording and playback. The TELMI (Technology Enhanced Learning of Musical Instrument Performance) Project seeks to design and implement new interaction paradigms for music learning and training based on state-of-the-art multimodal (audio, image, video, and motion) technologies. The project focuses on the violin as a case study. This practice work is intended as demo, showing to MOCO attendants the results the project obtained along two years of work. The demo simulates a setup at a higher education music institution, where attendants with any level of previous violin experience (and even with no experience at all) are invited to try the technologies themselves, performing basic tests of violin skill and pre-defined exercises under the guidance of the researchers involved in the project.