Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity. However, most entity recognition systems in the music domain have concentrated on formal texts (e.g. artists' biographies, encyclopedic articles, etc.), ignoring rich and noisy user-generated content. In this work, we present a novel method to recognize musical entities in Twitter content generated by users following a classical music radio channel. Our approach takes advantage of both formal radio schedule and users' tweets to improve entity recognition. We instantiate several machine learning algorithms to perform entity recognition combining task-specific and corpus-based features. We also show how to improve recognition results by jointly considering formal and user-generated content
Music Information Retrieval (MIR) is an interdisciplinary field which borrows tools of several disciplines, such as signal processing, musicology, machine learning, psychology and many others, for extracting knowledge from musical objects (be them audio, texts, etc.) @cite_0 . In the last decade, several MIR tasks have benefited from NLP, such as sound and music recommendation @cite_23 , automatic summary of song review @cite_2 , artist similarity @cite_14 and genre classification @cite_6 .
@cite_14 @cite_6 @cite_0 @cite_23 @cite_2
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Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research. Musical genre and mood classification techniques combining lyric and audio features for Music Information Retrieval (MIR) have been studied widely in recent years. This paper investigates the performances of musical genre and mood classification using only lyric features. In this preliminary study, the Part-of-Speech (POS) feature is utilizes for classification of a collection of 600 songs. Ten musical genre and mood categories were selected respectively based on a summary from the literature. Experiments show that classification accuracies for mood categories outperform genres. Content-based music information retrieval tasks are typically solved with a two-stage approach: features are extracted from music audio signals, and are then used as input to a regressor or classifier. These features can be engineered or learned from data. Although the former approach was dominant in the past, feature learning has started to receive more attention from the MIR community in recent years. Recent results in feature learning indicate that simple algorithms such as K-means can be very effective, sometimes surpassing more complicated approaches based on restricted Boltzmann machines, autoencoders or sparse coding. Furthermore, there has been increased interest in multiscale representations of music audio recently. Such representations are more versatile because music audio exhibits structure on multiple timescales, which are relevant for different MIR tasks to varying degrees. We develop and compare three approaches to multiscale audio feature learning using the spherical K-means algorithm. We evaluate them in an automatic tagging task and a similarity metric learning task on the Magnatagatune dataset. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. How individuals perceive music is influenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs’ text and its sound. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyrics, may thus be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents – many lyrics are for example composed in rhyming verses, and may have different frequencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use ‘slang’ language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word verse length, and the amount of repetative text. In this paper, we present a novel set of features developed for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classification on a test collection in order to demonstrate our analysis. Many if not most audio features used in MIR research are inspired by work done in speech recognition and are variations on the spectrogram. Recently, much attention has been given to new representations of audio that are sparse and time-relative. These representations are efficient and able to avoid the time-frequency trade-off of a spectrogram. Yet little work with music streams has been conducted and these features remain mostly unused in the MIR community. In this paper we further explore the use of these features for musical signals. In particular, we investigate their use on realistic music examples (i.e. released commercial music) and their use as input features for supervised learning. Furthermore, we identify three specific issues related to these features which will need to be further addressed in order to obtain the full benefit for MIR applications. This paper aims at leveraging microblogs to address two challenges in music information retrieval (MIR), similarity estimation between music artists and inferring typical listening patterns at different granularity levels (city, country, global). From two collections of several million microblogs, which we gathered over ten months, music-related information is extracted and statistically analyzed. We propose and evaluate four co-occurrence-based methods to compute artist similarity scores. Moreover, we derive and analyze culture-specific music listening patterns to investigate the diversity of listening behavior around the world. We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the music information retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms. This paper first presents a series of experiments carried out with two state-of-the-art methods for cover song identification. We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or dynamic time warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best performing ones are finally applied to the newly proposed method. Multiple evaluations of this one confirm a large increase in identification accuracy when comparing it with alternative state-of-the-art approaches. There has been an increasing attention on learning feature representations from the complex, high-dimensional audio data applied in various music information retrieval (MIR) problems. Unsupervised feature learning techniques, such as sparse coding and deep belief networks have been utilized to represent music information as a term-document structure comprising of elementary audio codewords. Despite the widespread use of such bag-of-frames (BoF) model, few attempts have been made to systematically compare different component settings. Moreover, whether techniques developed in the text retrieval community are applicable to audio codewords is poorly understood. To further our understanding of the BoF model, we present in this paper a comprehensive evaluation that compares a large number of BoF variants on three different MIR tasks, by considering different ways of low-level feature representation, codebook construction, codeword assignment, segment-level and song-level feature pooling, tf-idf term weighting, power normalization, and dimension reduction. Our evaluations lead to the following findings: 1) modeling music information by two levels of abstraction improves the result for difficult tasks such as predominant instrument recognition, 2) tf-idf weighting and power normalization improve system performance in general, 3) topic modeling methods such as latent Dirichlet allocation does not work for audio codewords. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval (MIR) field, where current approaches rely on the analysis of the good-quality audio material. This work addresses a real-world scenario with several research challenges, i.e. the analysis of user-generated videos that are varied in terms of recording conditions and quality and may contain multiple instruments sounding simultaneously and background noise. Our approach does not only focus on the analysis of audio information, but we exploit the multimodal information embedded in the audio and visual domains. In order to do so, we develop a Convolutional Neural Network (CNN) architecture which combines learned representations from both modalities at a late fusion stage. Our approach is trained and evaluated on two large-scale video datasets: YouTube-8M and FCVID. The proposed architectures demonstrate state-of-the-art results in audio and video object recognition, provide additional robustness to missing modalities, and remains computationally cheap to train. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] This paper describes the design policy and specifications of the RWC Music Database, a copyright-cleared music database (DB) compiled specifically for research purposes. Shared DBs are common in other research fields and have made significant contributions to progress in those fields. The field of music information processing, however, has lacked a common DB of musical pieces or a large-scale DB of musical instrument sounds. We therefore recently constructed the RWC Music Database comprising four original component DBs: the Popular Music Database (100 pieces), Royalty-Free Music Database (15 pieces), Classical Music Database (50 pieces), and Jazz Music Database (50 pieces). In this paper we report the construction of two additional component DBs: the Music Genre Database (100 pieces) and Musical Instrument Sound Database (50 instruments). For all 315 musical pieces, we prepared original audio signals, corresponding standard MIDI files, and text files of lyrics (for songs). For all 50 instruments, we recorded individual sounds at half-tone intervals with several variations of playing styles, dynamics, instrument manufacturers, and musicians. It is our hope that our DB will make a significant contribution to future advances in the field of music information processing. Central to the field of MIR research is the evaluation of algorithms used to extract information from music data. We present mir_eval, an open source software library which provides a transparent and easy-to-use implementation of the most common metrics used to measure the performance of MIR algorithms. In this paper, we enumerate the metrics implemented by mir_eval and quantitatively compare each to existing implementations. When the scores reported by mir_eval differ substantially from the reference, we detail the differences in implementation. We also provide a brief overview of mir_eval’s architecture, design, and intended use. 1. EVALUATING MIR ALGORITHMS Much of the research in Music Information Retrieval (MIR) involves the development of systems that process raw music data to produce semantic information. The goal of these systems is frequently defined as attempting to duplicate the performance of a human listener given the same task [5]. A natural way to determine a system’s effectiveness might be for a human to study the output produced by the system and judge its correctness. However, this would yield only subjective ratings, and would also be extremely timeconsuming when evaluating a system’s output over a large corpus of music. Instead, objective metrics are developed to provide a well-defined way of computing a score which indicates each system’s output’s correctness. These metrics typically involve a heuristically-motivated comparison of the system’s output to a reference which is known to be correct. Over time, certain metrics have become standard for each ∗Please direct correspondence to craffel@gmail.com c © Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis. This text offers a personal and very subjective view on the current situation of Music Information Research (MIR). Motivated by the desire to build systems with a somewhat deeper understanding of music than the ones we currently have, I try to sketch a number of challenges for the next decade of MIR research, grouped around six simple truths about music that are probably generally agreed on but often ignored in everyday research. In this paper we demonstrate the artist detection component of Minnowmatch, a machine listening and music retrieval engine. Minnowmatch (Mima) automatically determines various meta-data and makes classifications concerning a piece of audio using neural networks and support vector machines. The technologies developed in Minnowmatch may be used to create audio information retrieval systems, copyright protection devices, and recommendation agents. This paper concentrates on the artist or source detection component of Mima, which we show to classify a one-in-n artist space correctly 91 over a small song-set and 70 over a larger song set. We show that scaling problems using only neural networks for classification can be addressed with a pre-classification step of multiple support vector machines. We introduce a dataset for facilitating audio-visual analysis of music performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground-truth annotation files including frame-level and note-level transcriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches to address the challenges involved in maintaining synchronization and expressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset with existing widely used music audio datasets. We anticipate that the dataset will be useful for the development and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multimodal tasks. We benchmark two existing MIR tasks (multipitch analysis and score-informed source separation) on the dataset and compare them with other existing music audio datasets. In addition, we consider two novel multimodal MIR tasks (visually informed multipitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measurements and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables. Advances in the science and technology of Music Information Retrieval (MIR) systems and algorithms are dependent on the development of rigorous measures of accuracy and performance such that meaningful comparisons among current and novel approaches can be made. This is the motivating principle driving the efforts of the International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) and the annual Music Information Retrieval Evaluation eXchange (MIREX). Since it started in 2005, MIREX has fostered great advancements not only in many specific areas of MIR, but also in our general understanding of how MIR systems and algorithms are to be evaluated. This chapter outlines some of the major highlights of the past four years of MIREX evaluations, including its organizing principles, the selection of evaluation metrics, and the evolution of evaluation tasks. The chapter concludes with a brief introduction of how MIREX plans to expand into the future using a suite of Web 2.0 technologies to automated MIREX evaluations. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.