Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

Ten years of MIREX: reflections, challenges and opportunities

The Music Information Retrieval Evaluation eXchange (MIREX) has been run annually since 2005, with the October 2014 plenary marking its tenth iteration. By 2013, MIREX has evaluated approximately 2000 individual music information retrieval (MIR) algorithms for a wide range of tasks over 37 different test collections. MIREX has involved researchers from over 29 different contrives with a median of 109 individual participants per year. This pater summarizes the history of MIREX form its earliest planning meeting in 2001 to the present. It reflects upon the administrative, financial, and technological challenges MIREX has faced and describes how those challenges have been surmounted. We propose new funding models, a distributed evaluation framework, and more holistic user experience evaluation tasks-some evolutionary, some revolutionary-for the continued success of MIREX. We hope that this paper will inspire MIR community members to contribute their ideas so MIREX can have many more successful years to come

The impact of MIREX on scholarly research (2005-2010)

This paper explores the impact of the MIREX (Music Information Retrieval Evaluation eXchange) evaluation initiative on scholarly research. Impact is assessed through a bibliometric evaluation of both the MIREX extended abstracts and the papers citing the MIREX results, the trial framework and methodology, or MIREX datasets. Impact is examined through number of publications and citation analysis. We further explore the primary publication venues for MIREX results, the geographic distribution of both MIREX contributors and researchers citing MIREX results, and the spread of MIREX-based research beyond the MIREX contributor teams. This analysis indicates that research in this area is highly collaborative, has achieved an international dissemination, and has grown to have a significant profile in the research literature

Notes from the ISMIR 2012 late-breaking session on evaluation in music information retrieval

During the last day of the ISMIR 2012 conference there were two events related to Music IR Evaluation. A panel took place during the morning to discuss several issues concerning the various evaluation initiatives with the general audience at ISMIR. A late-breaking session during the afternoon kept the discussion alive between a group of researchers who wanted to dig deeper into these issues. This extended abstract reports the main topics covered during this short session and the general thoughts that came up

Musical Features for Automatic Music Transcription Evaluation

This technical report gives a detailed, formal description of the features introduced in the paper: Adrien Ycart, Lele Liu, Emmanouil Benetos and Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription", Transactions of the International Society for Music Information Retrieval (TISMIR), Accepted, 2020

Music Similarity Estimation

Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn

Publishing Music Similarity Features on the Semantic Web.

We describe the process of collecting, organising and publishing a large set of music similarity features produced by the SoundBite [10] playlist generator tool. These data can be a valuable asset in the development and evaluation of new Music Information Retrieval algorithms. They can also be used in Web-based music search and retrieval applications. For this reason, we make a database of features available on the Semantic Web via a SPARQL end-point, which can be used in Linked Data services. We provide examples of using the data in a research tool, as well as in a simple web application which responds to audio queries and finds a set of similar tracks in our database