A generalized matrix profile framework with support for contextual series analysis

The Matrix Profile is a state-of-the-art time series analysis technique that can be used for motif discovery, anomaly detection, segmentation and others, in various domains such as healthcare, robotics, and audio. Where recent techniques use the Matrix Profile as a preprocessing or modeling step, we believe there is unexplored potential in generalizing the approach. We derived a framework that focuses on the implicit distance matrix calculation. We present this framework as the Series Distance Matrix (SDM). In this framework, distance measures (SDM-generators) and distance processors (SDM-consumers) can be freely combined, allowing for more flexibility and easier experimentation. In SDM, the Matrix Profile is but one specific configuration. We also introduce the Contextual Matrix Profile (CMP) as a new SDM-consumer capable of discovering repeating patterns. The CMP provides intuitive visualizations for data analysis and can find anomalies that are not discords. We demonstrate this using two real world cases. The CMP is the first of a wide variety of new techniques for series analysis that fits within SDM and can complement the Matrix Profile

Inpainting of long audio segments with similarity graphs

We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e. the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals

Audio Content-Based Music Retrieval

The rapidly growing corpus of digital audio material requires novel retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their "specificity", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on "granularity", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise "audio identification", "audio matching", and "version identification". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework

Towards Automated Processing of Folk Song Recordings

Folk music is closely related to the musical culture of a specific nation or region. Even though folk songs have been passed down mainly by oral tradition, most musicologists study the relation between folk songs on the basis of symbolic music descriptions, which are obtained by transcribing recorded tunes into a score-like representation. Due to the complexity of audio recordings, once having the transcriptions, the original recorded tunes are often no longer used in the actual folk song research even though they still may contain valuable information. In this paper, we present various techniques for making audio recordings more easily accessible for music researchers. In particular, we show how one can use synchronization techniques to automatically segment and annotate the recorded songs. The processed audio recordings can then be made accessible along with a symbolic transcript by means of suitable visualization, searching, and navigation interfaces to assist folk song researchers to conduct large scale investigations comprising the audio material

Learner-generated comic (lgc): a production model

Recent advancement of authoring tools has fostered widespread interest towards using comics as a Digital Storytelling medium. This technology integrated learning approach is known as Learner-Generated Comic (LGC) production; where learners' knowledge and ideas on various subjects are synthesized in a form of digital educational comic. Despite the prior evidences for the didactic values of LGC production, most scholars do not emphasise on a quality, theoretically supported, and strategic LGC production methodology that accommodate to interrelated key elements and production methods of LGC. As a result, there is a tendency to view LGC production as challenging and impractical. Essentially, there is a lack of conceptual models and methods that comprehensively tailor the crucial theories, elements, techniques, technological, and systematic processes of LGC production. Within this context, this study attempts to propose LGC production model that serves as systematic approach which includes the fundamental components for learners to produce digital educational comics. Therefore, in order to accomplish the main aim, a number of sub objectives are formed: (1) to determine the core components for LGC production model, (2) to construct a systematic LGC production model based on the identified components, (3) to evaluate the proposed LGC production model, and (4) to assess the LGC products developed by the proposed model users. This study adopts the Design Science Research methodology as the framework of the research process. Activities of LGC production model construction include literature review and comparative study, expert consultation, and user participation. The proposed model is evaluated through user experience testing and expert review. Results from hypothesis testing concludes that the proposed LGC production model is significantly perceived as having quality in serving as a guideline for learners to design and develop digital educational comics. It was also found that the proposed model has been well-accepted by local and international experts. In addition, assessment of LGC products developed from the user experience testing has implicated there are significance differences between LGC products developed by the proposed model users and non-users. In conclusion, adoption of a systematic, scholarly grounded, and authenticated LGC production model can contribute to the planning, implementation, and evaluation of Digital Storytelling session that enhance learning experience through LGC design and development

Feature Extraction for Music Information Retrieval

Copyright c © 2009 Jesper Højvang Jensen, except where otherwise stated