Transactions of the International Society for Music Information Retrieval最新文献

{"title":"Perceptual and automated estimates of infringement in 40 music copyright cases","authors":"Yuchen Yuan, Charles Cronin, Daniel Müllensiefen, Shinya Fujii, Patrick E. Savage","doi":"10.5334/tismir.151","DOIUrl":"https://doi.org/10.5334/tismir.151","url":null,"abstract":"Music copyright infringement lawsuits implicate millions of dollars in damages and costs of litigation. There are, however, few objective measures by which to evaluate these claims. Recent music information retrieval research has proposed objective algorithms to automatically detect musical similarity, which might reduce subjectivity in music copyright infringement decisions, but there remains minimal relevant perceptual data despite its crucial role in copyright law. We collected perceptual data from 51 participants for 40 adjudicated copyright cases from 1915–2018 in 7 legal jurisdictions (USA, UK, Australia, New Zealand, Japan, People’s Republic of China, and Taiwan). Each case was represented by three different versions: either full audio, melody only (MIDI), or lyrics only (text). Due to the historical emphasis in legal opinions on melody as the key criterion for deciding infringement, we originally predicted that listening to melody-only versions would result in perceptual judgments that more closely matched actual past legal decisions. However, as in our preliminary study of 17 court decisions (Yuan et al., 2020), our results did not match these predictions. Participants listening to full audio outperformed not only the melody-only condition, but also automated algorithms designed to calculate musical similarity (with maximal accuracy of 83% vs. 75%, respectively). Meanwhile, lyrics-only conditions performed at chance levels. Analysis of outlier cases suggests that music, lyrics, and contextual factors can interact in complex ways difficult to capture using quantitative metrics. We propose directions for further investigation including using larger and more diverse samples of cases, enhanced methods, and adapting our perceptual experiment method to avoid relying on ground truth data only from court decisions (which may be subject to errors and selection bias). Our results contribute data and methods to inform practical debates relevant to music copyright law throughout the world, such as the question of whether, and the extent to which, judges and jurors should be allowed to hear published sound recordings of the disputed works in determining musical similarity. Our results ultimately suggest that while automated algorithms are unlikely to replace human judgments, they may help to supplement them.","PeriodicalId":485306,"journal":{"name":"Transactions of the International Society for Music Information Retrieval","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135481555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Four-way Classification of Tabla Strokes with Transfer Learning Using Western Drums","authors":"Rohit M. Ananthanarayana, Amitrajit Bhattacharjee, Preeti Rao","doi":"10.5334/tismir.150","DOIUrl":"https://doi.org/10.5334/tismir.150","url":null,"abstract":"Motivated by the musicological relevance of tabla stroke categories in tabla accompaniment playing, we present an automatic four-way stroke classification system based on convolutional neural networks, while recognising the challenge of instrument- and style-independent classification with limited available labeled training data. Tabla stroke transcription has been traditionally viewed as a monophonic timbre recognition task given the variety of musically distinct single-drum and two-drum strokes that comprise the music. In this work, we adopt a more sound-production based approach by identifying a reduced set of ‘atomic’ strokes (damped, resonant treble and resonant bass) that serve as the primary level for classification. An advantage of this is the better exploitation of tabla training data and the potential for better generalization. The new viewpoint also facilitates exploring the acoustic similarity with Western drums via the investigation of transfer learning for the tabla task. We find that the drum pretraining learns features that are useful for our tabla stroke classification task. Further fine-tuning the model with the target tabla data leads to the expected improvements in performance, which, however, surpasses that achieved with a purely tabla-trained model for only one of the stroke categories.","PeriodicalId":485306,"journal":{"name":"Transactions of the International Society for Music Information Retrieval","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135595119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Piano Concerto Dataset (PCD): A Multitrack Dataset of Piano Concertos","authors":"Yigitcan Özer, Simon Schwär, Vlora Arifi-Müller, Jeremy Lawrence, Emre Sen, Meinard Müller","doi":"10.5334/tismir.160","DOIUrl":"https://doi.org/10.5334/tismir.160","url":null,"abstract":"The piano concerto is a genre of central importance in Western classical music, often consisting of a virtuoso solo part for piano and an orchestral accompaniment. In this article, we introduce the Piano Concerto Dataset (PCD), which comprises a collection of excerpts with separate piano and orchestral tracks from piano concertos ranging from the Baroque to the Post-Romantic era. In particular, using existing backing tracks by the music publisher Music Minus One, we recorded excerpts from 15 different piano concertos played by five interpreters on various instruments under different acoustic conditions. The key challenge of playing along with pre-recorded orchestral accompaniments lies in the exact synchronization of the performer. For guiding the pianists for obtaining a high synchronization accuracy, we used additional click tracks generated with measure and beat annotations of the orchestral tracks, which also are provided in the PCD. The dataset is relevant for a variety of Music Information Retrieval (MIR) tasks, including music source separation, automatic accompaniment, music synchronization, editing, and upmixing.","PeriodicalId":485306,"journal":{"name":"Transactions of the International Society for Music Information Retrieval","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135441488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology","authors":"Christof Weiß, Vlora Arifi-Müller, Michael Krause, Frank Zalkow, Stephanie Klauk, Rainer Kleinertz, Meinard Müller","doi":"10.5334/tismir.161","DOIUrl":"https://doi.org/10.5334/tismir.161","url":null,"abstract":"This paper introduces the Wagner Ring Dataset (WRD), a multi-modal and multi-version resource on the large-scale opera cycle Der Ring des Nibelungen by Richard Wagner. The Ring comprises four music dramas organized into eleven acts and 21 939 measures in total. Concerning sheet music, we processed a publicly available piano reduction (822 pages) of the full score with optical music recognition followed by extensive manual corrections to create a high-quality, machine-readable symbolic score. Concerning audio data, our corpus covers 16 recorded performances of the full Ring (three of which are publicly available thanks to copyright expiry), each lasting about 14–15 hours. To musically synchronize these versions among each other, we manually annotated all measure positions for three performances, which we transferred to the remaining performances via automated synchronization techniques. The dataset further comprises annotations of key and time signatures, scenes, and singing voice regions (libretto). Moreover, we provide note event annotations for all performances derived from the piano score. The WRD thus constitutes a comprehensive resource for developing algorithms for various music information retrieval tasks, complementing existing datasets with a complex opera scenario. For computational musicology, the WRD serves as a structured dataset that allows for studying the composition and performances of the Ring.","PeriodicalId":485306,"journal":{"name":"Transactions of the International Society for Music Information Retrieval","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135210849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PiJAMA: Piano Jazz with Automatic MIDI Annotations","authors":"Drew Edwards, Simon Dixon, Emmanouil Benetos","doi":"10.5334/tismir.162","DOIUrl":"https://doi.org/10.5334/tismir.162","url":null,"abstract":"Recent advances in automatic piano transcription have enabled large scale analysis of piano music in the symbolic domain. However, the research has largely focused on classical piano music. We present PiJAMA (Piano Jazz with Automatic MIDI Annotations): a dataset of over 200 hours of solo jazz piano performances with automatically transcribed MIDI. In total there are 2,777 unique performances by 120 different pianists across 244 recorded albums. The dataset contains a mixture of studio recordings and live performances. We use automatic audio tagging to identify applause, spoken introductions, and other non-piano audio to facilitate downstream music information retrieval tasks. We explore descriptive statistics of the MIDI data, including pitch histograms and chromaticism. We then demonstrate two experimental benchmarks on the data: performer identification and generative modeling. The dataset, including a link to the associated source code is available at https://almostimplemented.github.io/PiJAMA/.","PeriodicalId":485306,"journal":{"name":"Transactions of the International Society for Music Information Retrieval","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135496207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}