Alignment and Identification of Multimedia Data: Application to Music and Gesture Processing (2012)

The overwhelming availability of large multimedia collections poses increasingly challenging research problems regarding the organization of, and access to data. A general consensus has been reached in the Information Retrieval community, asserting the need for tools that move past metadata-based techniques and exploit directly the information contained in the media. At the same time, interaction with content has evolved beyond the traditional passive enjoyment paradigm, bringing forth the demand for advanced control and manipulation options. The aim of this thesis is to investigate techniques for multimedia data alignment and identification. In particular, music audio streams and gesture-capture time series are considered. Special attention is given to the efficiency of the proposed approaches, namely the realtime applicability of alignment algorithms and the scalability of identification strategies. The concept of alignment refers to the identification and matching of corresponding substructures in related entities. The focus of this thesis is directed towards alignment of sequences with respect to a single dimension, aiming at the identification and matching of significant events in related time series. The alignment of audio recordings of music to their symbolic representations serves as a starting point to explore different methodologies based on statistical models. A unified model for the real time alignment of music audio streams to both symbolic scores and audio references is proposed. Its advantages are twofold: unlike most state-of-the-art systems, tempo is an explicit parameter within the stochastic framework; moreover, both alignment problems can be formulated within a common framework by exploiting a continuous representation of the reference content. A novel application of audio alignment techniques was found in the domain of studio recording productions, reducing the human effort spent in manual repetitive tasks. Gesture alignment is closely related to the domain of music alignment, as the artistic aims and engineering solutions of both areas largely overlap. Expressivity in gesture performance can be characterized by both the choice of a particular gesture and the way the gesture is executed. The former aspect involves a gesture recognition task, while the latter is addressed considering the time-evolution of features and the way these differ from pre-recorded templates. A model, closely related to the mentioned music alignment strategy, is proposed, capable of simultaneously recognizing a gesture among many templates and aligning it against the correct reference in realtime, while jointly estimating signal feature such as rotation, scaling, velocity. Due to the increasingly large volume of music collections, the organization of media items according to their perceptual characteristics has become of fundamental importance. In particular, content-based identification technologies provide the tools to retrieve and organize music documents. Music identification techniques should ideally be able to identify a recording -- by comparing it against a set of known recordings -- independently from the particular performance, even in case of significantly different arrangements and interpretations. Even though alignment techniques play a central role in many works of the music identification literature, the proposed methodology addresses the task using techniques that are usually associated to textual IR. Similarity computation is based on hashing, attempting at creating collisions between vectors that are close in the feature space. The resulting compactness of the representation of audio content allows index-based retrieval strategies to be exploited for maximizing computational efficiency. A particular application is considered, regarding Cultural Heritage preservation institutions. A methodology is proposed to automatically identify recordings in collections of digitized tapes and vinyl discs. This scenario differs significantly from that of a typical identification task, as a query most often contains more than one relevant result (distinct music work). The audio alignment methodology mentioned above is finally exploited to carry out a precise segmentation of recordings into their individual tracks.