Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

Agile thinking in motion graphics practice and its potential for design education

Motion Graphics is relatively new subject and its methodologies are still being developed. There are useful lessons to be learnt from the practice in early cinema from the 1890's to the 1930's where Agile thinking was used by a number of practitioners including Fritz Lang. Recent studies in MA Motion Graphics have accessed some of this thinking incorporating them in a series of Motion Graphic tests and experiments culminating in a two minute animation “1896 Olympic Marathon”. This paper demonstrates how the project and its design methodology can contribute new knowledge for the practice and teaching of this relatively new and expanding area of Motion Graphic Design. This would be not only invaluable to the International community of Motion Graphic practitioners, Educators and Researchers in their development of this maturing field. But also to the broader Multidisciplinary disciplines within Design Education. These methodologies have been arrived at by accessing the work of creative and reflective practice as defined by Carol Grey and Julian Marlin in Visualizing Research (2004) and reflective practice as defined by Donald Schon (1983). Central to the investigation has been the approach of Agile thinking from the methodology of "Bricolage" by Levi Strauss "The Savage Mind" (1966)

Methodological framework and design process for applying evolutionary simulation to musical interactions

This paper focuses on a methodological framework where the creative design process evolves through iterative cycles. The design process undertakes a complex network of tasks for integrating two domain models: dynamical simulation and musical interaction. The framework accounts for engi-neering technical and compositional affordances to accom-modate evolving behaviors to be expressed in real time per-formance interplay. This is illustrated with a case study of simulated swarms of heterogeneous agents. Highly integrat-ed parallel work streams are elucidated with sub-process elicitation in simulation, system integration and software engineering, composition, and performance. Framework formalization draws upon the established RAD model with significant modification to present the extended version that can be multi-threaded for concurrent creative processes. Two landmarks of 20th century music automation are drawn diachronically to frame the technical discussion in a social context of listening practice, developed by modeling crea-tive process and testing musical assumptions. Revisited cannon is redirected from bygone exemplars to ongoing practice, illuminating three baseline requirements for a methodological framework: interdisciplinary platform archi-tecture, complex systems model of music creation, and agile listening. Concluding theses on second order listening and interdisciplinary architecture summarize the proposed methodological framework addressing contextual listening and technical culture

Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis

Anomaly detection in multidimensional data is a challenging task. Detecting anomalous mobility patterns in a city needs to take spatial, temporal, and traffic information into consideration. Although existing techniques are able to extract spatiotemporal features for anomaly analysis, few systematic analysis about how different factors contribute to or affect the anomalous patterns has been proposed. In this paper, we propose a novel technique to localize spatiotemporal anomalous events based on tensor decomposition. The proposed method employs a spatial-feature-temporal tensor model and analyzes latent mobility patterns through unsupervised learning. We first train the model based on historical data and then use the model to capture the anomalies, i.e., the mobility patterns that are significantly different from the normal patterns. The proposed technique is evaluated based on the yellow-cab dataset collected from New York City. The results show several interesting latent mobility patterns and traffic anomalies that can be deemed as anomalous events in the city, suggesting the effectiveness of the proposed anomaly detection method

Skyler and Bliss

Hong Kong remains the backdrop to the science fiction movies of my youth. The city reminds me of my former training in the financial sector. It is a city in which I could have succeeded in finance, but as far as art goes it is a young city, and I am a young artist. A frustration emerges; much like the mould, the artist also had to develop new skills by killing off his former desires and manipulating technology. My new series entitled HONG KONG surface project shows a new direction in my artistic research in which my technique becomes ever simpler, reducing the traces of pixelation until objects appear almost as they were found and photographed. Skyler and Bliss presents tectonic plates based on satellite images of the Arctic. Working in a hot and humid Hong Kong where mushrooms grow ferociously, a city artificially refrigerated by climate control, this series provides a conceptual image of a imaginary typographic map for survival. (Laurent Segretier

Controllability and explainability in a hybrid social recommender system

The growth in artificial intelligence (AI) technology has advanced many human-facing applications. The recommender system is one of the promising sub-domain of AI-driven application, which aims to predict items or ratings based on user preferences. These systems were empowered by large-scale data and automated inference methods that bring useful but puzzling suggestions to the users. That is, the output is usually unpredictable and opaque, which may demonstrate user perceptions of the system that can be confusing, frustrating or even dangerous in many life-changing scenarios. Adding controllability and explainability are two promising approaches to improve human interaction with AI. However, the varying capability of AI-driven applications makes the conventional design principles are less useful. It brings tremendous opportunities as well as challenges for the user interface and interaction design, which has been discussed in the human-computer interaction (HCI) community for over two decades. The goal of this dissertation is to build a framework for AI-driven applications that enables people to interact effectively with the system as well as be able to interpret the output from the system. Specifically, this dissertation presents the exploration of how to bring controllability and explainability to a hybrid social recommender system, included several attempts in designing user-controllable and explainable interfaces that allow the users to fuse multi-dimensional relevance and request explanations of the received recommendations. The works contribute to the HCI fields by providing design implications of enhancing human-AI interaction and gaining transparency of AI-driven applications

Superquadric representation of scenes from multi-view range data

Object representation denotes representing three-dimensional (3D) real-world objects with known graphic or mathematic primitives recognizable to computers. This research has numerous applications for object-related tasks in areas including computer vision, computer graphics, reverse engineering, etc. Superquadrics, as volumetric and parametric models, have been selected to be the representation primitives throughout this research. Superquadrics are able to represent a large family of solid shapes by a single equation with only a few parameters. This dissertation addresses superquadric representation of multi-part objects and multiobject scenes. Two issues motivate this research. First, superquadric representation of multipart objects or multi-object scenes has been an unsolved problem due to the complex geometry of objects. Second, superquadrics recovered from single-view range data tend to have low confidence and accuracy due to partially scanned object surfaces caused by inherent occlusions. To address these two problems, this dissertation proposes a multi-view superquadric representation algorithm. By incorporating both part decomposition and multi-view range data, the proposed algorithm is able to not only represent multi-part objects or multi-object scenes, but also achieve high confidence and accuracy of recovered superquadrics. The multi-view superquadric representation algorithm consists of (i) initial superquadric model recovery from single-view range data, (ii) pairwise view registration based on recovered superquadric models, (iii) view integration, (iv) part decomposition, and (v) final superquadric fitting for each decomposed part. Within the multi-view superquadric representation framework, this dissertation proposes a 3D part decomposition algorithm to automatically decompose multi-part objects or multiobject scenes into their constituent single parts consistent with human visual perception. Superquadrics can then be recovered for each decomposed single-part object. The proposed part decomposition algorithm is based on curvature analysis, and includes (i) Gaussian curvature estimation, (ii) boundary labeling, (iii) part growing and labeling, and (iv) post-processing. In addition, this dissertation proposes an extended view registration algorithm based on superquadrics. The proposed view registration algorithm is able to handle deformable superquadrics as well as 3D unstructured data sets. For superquadric fitting, two objective functions primarily used in the literature have been comprehensively investigated with respect to noise, viewpoints, sample resolutions, etc. The objective function proved to have better performance has been used throughout this dissertation. In summary, the three algorithms (contributions) proposed in this dissertation are generic and flexible in the sense of handling triangle meshes, which are standard surface primitives in computer vision and graphics. For each proposed algorithm, the dissertation presents both theory and experimental results. The results demonstrate the efficiency of the algorithms using both synthetic and real range data of a large variety of objects and scenes. In addition, the experimental results include comparisons with previous methods from the literature. Finally, the dissertation concludes with a summary of the contributions to the state of the art in superquadric representation, and presents possible future extensions to this research

Music Synchronization, Audio Matching, Pattern Detection, and User Interfaces for a Digital Music Library System

Over the last two decades, growing efforts to digitize our cultural heritage could be observed. Most of these digitization initiatives pursuit either one or both of the following goals: to conserve the documents - especially those threatened by decay - and to provide remote access on a grand scale. For music documents these trends are observable as well, and by now several digital music libraries are in existence. An important characteristic of these music libraries is an inherent multimodality resulting from the large variety of available digital music representations, such as scanned score, symbolic score, audio recordings, and videos. In addition, for each piece of music there exists not only one document of each type, but many. Considering and exploiting this multimodality and multiplicity, the DFG-funded digital library initiative PROBADO MUSIC aimed at developing a novel user-friendly interface for content-based retrieval, document access, navigation, and browsing in large music collections. The implementation of such a front end requires the multimodal linking and indexing of the music documents during preprocessing. As the considered music collections can be very large, the automated or at least semi-automated calculation of these structures would be recommendable. The field of music information retrieval (MIR) is particularly concerned with the development of suitable procedures, and it was the goal of PROBADO MUSIC to include existing and newly developed MIR techniques to realize the envisioned digital music library system. In this context, the present thesis discusses the following three MIR tasks: music synchronization, audio matching, and pattern detection. We are going to identify particular issues in these fields and provide algorithmic solutions as well as prototypical implementations. In Music synchronization, for each position in one representation of a piece of music the corresponding position in another representation is calculated. This thesis focuses on the task of aligning scanned score pages of orchestral music with audio recordings. Here, a previously unconsidered piece of information is the textual specification of transposing instruments provided in the score. Our evaluations show that the neglect of such information can result in a measurable loss of synchronization accuracy. Therefore, we propose an OCR-based approach for detecting and interpreting the transposition information in orchestral scores. For a given audio snippet, audio matching methods automatically calculate all musically similar excerpts within a collection of audio recordings. In this context, subsequence dynamic time warping (SSDTW) is a well-established approach as it allows for local and global tempo variations between the query and the retrieved matches. Moving to real-life digital music libraries with larger audio collections, however, the quadratic runtime of SSDTW results in untenable response times. To improve on the response time, this thesis introduces a novel index-based approach to SSDTW-based audio matching. We combine the idea of inverted file lists introduced by Kurth and Müller (Efficient index-based audio matching, 2008) with the shingling techniques often used in the audio identification scenario. In pattern detection, all repeating patterns within one piece of music are determined. Usually, pattern detection operates on symbolic score documents and is often used in the context of computer-aided motivic analysis. Envisioned as a new feature of the PROBADO MUSIC system, this thesis proposes a string-based approach to pattern detection and a novel interactive front end for result visualization and analysis