Abstract The emergence of large multi‐institutional digital libraries has opened the door to aggregate‐level examinations of the published word. Such large‐scale analysis offers a new way to pursue traditional problems in the humanities and social sciences, using digital methods to ask routine questions of large corpora. However, inquiry into multiple centuries of books is constrained by the burdens of scale, where statistical inference is technically complex and limited by hurdles to access and flexibility. This work examines the role that exploratory data analysis and visualization tools may play in understanding large bibliographic datasets. We present one such tool, HathiTrust+Bookworm, which allows multifaceted exploration of the multimillion work HathiTrust Digital Library, and center it in the broader space of scholarly tools for exploratory data analysis.

This research proposes a framework for music mood classification that uses multiple and complementary information sources, namely, music audio, lyric text, and social tags associated with music pieces. This article presents the framework and a thorough evaluation of each of its components. Experimental results on a large data set of 18 mood categories show that combining lyrics and audio significantly outperformed systems using audio‐only features. Automatic feature selection techniques were further proved to have reduced feature space. In addition, the examination of learning curves shows that the hybrid systems using lyrics and audio needed fewer training samples and shorter audio clips to achieve the same or better classification accuracies than systems using lyrics or audio singularly. Last but not least, performance comparisons reveal the relative importance of audio and lyric features across mood categories.

Music Information Retrieval (MIR) evaluation has traditionally focused on system‐centered approaches where components of MIR systems are evaluated against predefined data sets and golden answers (i.e., ground truth). There are two major limitations of such system‐centered evaluation approaches: (a) The evaluation focuses on subtasks in music information retrieval, but not on entire systems and (b) users and their interactions with MIR systems are largely excluded. This article describes the first implementation of a holistic user‐experience evaluation in MIR, the MIREX Grand Challenge, where complete MIR systems are evaluated, with user experience being the single overarching goal. It is the first time that complete MIR systems have been evaluated with end users in a realistic scenario. We present the design of the evaluation task, the evaluation criteria and a novel evaluation interface, and the data‐collection platform. This is followed by an analysis of the results, reflection on the experience and lessons learned, and plans for future directions.

Conducting valid comparative evaluations of techniques in the field of Music Information Retrieval (MIR)presents particular challenges to MIR researchers due to issues of copyright and data sharing. Further, the interdisciplinary nature of MIR research and multi-faceted nature of human music perception make the sharing and reuse of techniques and implementations for particular facets of music perception and music information retrieval tasks highly desirable. In addition the field makes use of a diverse range of file formats, software environments and toolkits for extracting, encoding and accessing MIR data and services, making reuse extremely challenging. The NEMA project aims to provide the MIR field with a high-quality, secure and extensible workflow environment to facilitate: computation over remote audio and resource collections; optimal code reuse, interoperability between data formats and types, sharing and dissemination; standardised, high-quality evaluation procedures; and the encoding of metadata, data and results in a format suitable for distributed systems.

Cover song detection is becoming a very hot research topic when plentiful personal music recordings or performance are released on the Internet. A nice cover song recognizer helps us group and detect cover songs to improve the searching experience. The traditional detection is to match two musical audio sequences by exhaustive pairwise comparisons. Different from the existing work, our aim is to generate a group of concatenated feature sets based on regression modeling and arrange them by indexing-based approximate techniques to avoid complicated audio sequence comparisons. We mainly focus on using exact locality sensitive mapping (ELSM) to join the concatenated feature sets and soft hash values. Similarity-invariance among audio sequence comparison is applied to define an optimal combination of several audio features. Soft hash values are pre-calculated to help locate searching range more accurately. Furthermore, we implement our algorithms in analyzing the real audio cover songs and grouping and detecting a batch of relevant cover songs embedded in large audio datasets.

We develop a content-based audio COver Song IdeNtification (COSIN) system to detect/group cover songs.The COSIN takes music audio content as input and performs similarity searching to locate variants of the input (i.e., cover versions). Identified cover songs are returned in the rank order according to their similarity to the input.The COSIN also incorporates a set of tools to evaluate retrieval performance so researchers can explore different retrieval schemes and parameters (e.g. recall, precision).The COSIN utilizes a suite of techniques to detect cover songs including: Pitch + Dynamic Programming (DP), Chroma + DP, and Semantic Feature Summarization (SFS) + Hash-Based Approximate Matching (HBAM). Demonstration system shows that COSIN is a very potential music content retrieval tool. Running some music retrieval schemes on COSIN platform, recent experiments with SFS + LSH Variants demonstrate a nicely balanced efficiency (search speed) v. performance (search accuracy) tradeoff.

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The objective of the International Music Information Retrieval Systems Evaluation Laboratory project (IMIRSEL) is the establishment of the necessary resources for the scientifically valid development and evaluation of emerging Music Information Retrieval (MIR) and Music Digital Library (MDL) techniques and technologies. IMIRSEL is located at the Graduate School of Library and Information Science (GSLIS) at the University of Illinois at Urbana- Champaign (UIUC). Project Principal Investigator is J. Stephen Downie of GSLIS and Co-Principal Investigator is Prof. Michael Welge of the Automated Learning Group (ALG) of the National Center for Supercomputing Applications (NCSA). A mission of the project is the creation of secure, yet accessible, terascale collections of music materials in a variety of audio, symbolic and metadata forms. These collections, when coupled with a set of standardized experimental tasks and standardized evaluation metrics, will allow members of the international MIR/ MDL research community to participate in the newly created, TREC-like, Music Information Retrieval Evaluation eXchange (MIREX) contests.

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This poster reports upon the ongoing efforts being made to establish TREC-like and other comprehensive evaluation paradigms within the Music IR (MIR) and Music Digital Library (MDL) research communities. The proposed research tasks are based upon expert opinion garnered from members of the Information Retrieval (IR), MDL and MIR communities with regard to the construction and implementation of scientifically valid evaluation frameworks.

We developed, and then evaluated, a music information retrieval (MIR) system based upon the intervals found within the melodies of a collection of 9354 folksongs. The songs were converted to an interval-only representation of monophonic melodies and then fragmented t into length-n subsections called n-grams. The length of these n-grams and the degree to which we precisely represent the intervals are variables analyzed in this paper. We constructed a collection of "musical word" databases using the text-based, SMART information retrieval system. A group of simulated queries, some of which contained simulated errors, was run against these databases. The results were evaluated using the normalized precision and normalized recall measures. Our concept of "musical words" shows great merit thus implying that useful MIR systems can be constructed simply and efficiently using pre-existing text-based information retrieval software. Second, this study is a formal and comprehensive evaluation of ...

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