Generation of Two-Voice Imitative Counterpoint from Statistical Models

Generating new music based on rules of counterpoint has been deeply studied in music informatics. In this article, we try to go further, exploring a method for generating new music based on the style of Palestrina, based on combining statistical generation and pattern discovery. A template piece is used for pattern discovery, and the patterns are selected and organized according to a probabilistic distribution, using horizontal viewpoints to describe melodic properties of events. Once the template is covered with patterns, two-voice counterpoint in a florid style is generated into those patterns using a first-order Markov model. The template method solves the problem of coherence and imitation never addressed before in previous research in counterpoint music generation. For constructing the Markov model, vertical slices of pitch and rhythm are compiled over a large corpus of dyads from Palestrina masses. The template enforces different restrictions that filter the possible paths through the generation process. A double backtracking algorithm is implemented to handle cases where no solutions are found at some point within a generation path. Results are evaluated by both information content and listener evaluation, and the paper concludes with a proposed relationship between musical quality and information content. Part of this research has been presented at SMC 2016 in Hamburg, Germany

A book recommendation system based on named entities

Recommendation systems are extensively used for suggesting new items to users and play an important role in the discovery of relevant new items, be it books, movies or music. An effective recommendation system should provide heterogeneous results and should not be biased towards only the most popular items. Books are particularly well-suited to content-based filtering as they are now widely available in digital formats which can allow various text mining approaches to dig out content related information. This paper presents a framework to develop a content-based recommendation system for books which can further be integrated with a collaborative filtering model. The proposed content-based recommender will use the Named Entities as the basic criteria to rank books and give recommendations

Transcribing Content from Structural Images with Spotlight Mechanism

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18

Novel music discovery concepts: user experience and design implications

Current music consumers are facing an almost endless selection of music in online services to be accessed on-demand with a variety of devices. The focus has now shifted from providing on-demand access to massive music catalogs towards improving the user experience of the music services, providing new ways of finding relevant music from the massive online catalogs, and making music consumption a pleasurable experience. The key differentiation aspects for music services come largely from the user interface and the ways that music can be found or consumed. This thesis belongs to the fields of human-computer interation (HCI) and music information retrieval (MIR). HCI is concerned with the design, evaluation and implementation of interactive computing systems and MIR targets to broaden the understanding and usage of musical data through research, applications and tools. This thesis studies novel concepts for music discovery that are based on strong visual metaphors and stereotypes. The goal is to research the user experience (UX) of novel music discovery services and to formulate key design implications to support service development for music discovery. The research of music discovery prototypes consisted of three main phases: initial concept design phase, playful concept exploration phase, and iterative concept design phase. The thesis introduces, in total, ten prototype implementations of these novel concepts for music discovery. User evaluations of the implemented prototypes were conducted with Finnish active music listeners with both qualitative and quantitative research methods. This thesis contributes to both academic research on HCI in MIR and commercial music discovery service development. The results provide insights to user experience with different types of novel music discovery services. Five novel music discovery services using the same content-based music recommendation back-end were compared and the comparison results are reported including both first impressions and longer-term usage. Additionally, the results of the studies introduce a wide set of future directions for each music discovery approach. These future directions enable service developers to further enhance the music discovery experience within these fields. All but one of the proposed music discovery concepts work well for music discovery. The use of avatar characters and mood pictures for music discovery are the most promising ones. The results show that visual music discovery services have the potential to replace traditional music discovery services in different types of music discovery practices. The final contribution of the thesis is a set of 16 design implications for music discovery service developers

UNDERSTANDING MUSIC TRACK POPULARITY IN A SOCIAL NETWORK

Thousands of music tracks are uploaded to the Internet every day through websites and social networks that focus on music. While some content has been popular for decades, some tracks that have just been released have been ignored. What makes a music track popular? Can the duration of a music track’s popularity be explained and predicted? By analysing data on the performance of a music track on the ranking charts, coupled with the creation of machine-generated music semantics constructs and a variety of other track, artist and market descriptors, this research tests a model to assess how track popularity and duration on the charts are determined. The dataset has 78,000+ track ranking observations from a streaming music service. The importance of music semantics constructs (genre, mood, instrumental, theme) for a track, and other non-musical factors, such as artist reputation and social information, are assessed. These may influence the staying power of music tracks in online social networks. The results show it is possible to explain chart popularity duration and the weekly ranking of music tracks. This research emphasizes the power of data analytics for knowledge discovery and explanation that can be achieved with a combination of machine-based and econometrics-based approaches

Music recommendation: audio neighbourhoods to discover music in the long tail.

Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already know, newly released tracks, and the more niche music found in the 'long tail' of on-line music. Tag-based recommenders are not effective in this 'long tail' because relatively few people are listening to these tracks and so tagging tends to be sparse. However, similarity neighbourhoods in audio space can provide additional tag knowledge that is useful to augment sparse tagging. A new recommender exploits the combined knowledge, from audio and tagging, using a hybrid representation that extends the track's tag-based representation by adding semantic knowledge extracted from the tags of similar music tracks. A user evaluation and a larger experiment using Last.fm user data both show that the new hybrid recommender provides better quality recommendations than using only tags, together with a higher level of discovery of unknown and niche music. This approach of augmenting the representation for items that have missing information, with corresponding information from similar items in a complementary space, offers opportunities beyond content-based music recommendation

Developing music teacher identities: an international multi-site study

This study investigates pre-service music teacher’s (PSMT) perceptions of their professional identities. University-level education students in the United States America (USA), Spain and Australia were all asked interview questions based on general themes relevant to teacher identity development, and their responses were subjected to content analysis. Similarities were found in their perceptions of the role of ‘music teacher’ and their pre-university experiences/influences. Across the sites it seems that there was a dynamic and shifting relationship between PSMTs’ understandings of themselves as ‘musicians’ or as ‘teachers’ during their university years. This study confirms previous research in the area and contributes to the field in its discovery that these themes are found across three international sites. Implications of the findings are discussed and recommendations made for future research and practice

A comparative evaluation of algorithms for discovering translational patterns in Baroque keyboard works

We consider the problem of intra-opus pattern discovery, that is, the task of discovering patterns of a specified type within a piece of music. A music analyst undertook this task for works by Domenico Scarlattti and Johann Sebastian Bach, forming a benchmark of 'target' patterns. The performance of two existing algorithms and one of our own creation, called SIACT, is evaluated by comparison with this benchmark. SIACT out-performs the existing algorithms with regard to recall and, more often than not, precision. It is demonstrated that in all but the most carefully selected excerpts of music, the two existing algorithms can be affected by what is termed the 'problem of isolated membership'. Central to the relative success of SIACT is our intention that it should address this particular problem. The paper contrasts string-based and geometric approaches to pattern discovery, with an introduction to the latter. Suggestions for future work are given