The Songs of Our Past

Advancements in technology have resulted in unique changes in the way people interact with music today: Small, portable devices allow listening to it everywhere and provide access to thousands or, via streaming, even millions of songs. In addition, all played tracks can be logged with an accuracy down to the second. So far, these music listening histories are mostly used for music recommendation and hidden from their actual creators. But people may also benefit from this data more directly: as memory extensions that allow retrieving the name of a title, for rediscovering old favorites and reflecting about their lives. Additionally, listening histories can be representations of the implicit relationships between musical items. In this thesis, I discuss the contents of these listening histories and present software tools that give their owners the chance to work with them. As a first approach to understanding the patterns contained in listening histories I give an overview of the relevant literature from musicology, human-computer-interaction and music information retrieval. This literature review identifies the context as a main influence for listening: from the musical and temporal to the demographical and social. I then discuss music listening histories as digital memory extensions and a part of lifelogging data. Based on this notion, I present what an ideal listening history would look like and how close the real-world implementations come. I also derive a design space, centered around time, items and listeners, for this specific type of data and shortcomings of the real-world data regarding the previously identified contextual factors. The main part of this dissertation describes the design, implementation and evaluation of visualizations for listening histories. The first set of visualizations presents listening histories in the context of lifelogging, to allow analysing one’s behavior and reminiscing. These casual information visualizations vary in complexity and purpose. The second set is more concerned with the musical context and the idea that listening histories also represent relationships between musical items. I present approaches for improving music recommendation through interaction and integrating listening histories in regular media players. The main contributions of this thesis to HCI and information visualization are: First, a deeper understanding of relevant aspects and important patterns that make a person’s listening special and unique. Second, visualization prototypes and a design space of listening history visualizations that show approaches how to work with temporal personal data in a lifelogging context. Third, ways to improve recommender systems and existing software through the notion of seeing relationships between musical items in listening histories. Finally, as a meta-contribution, the casual approach of all visualizations also helps in providing non-experts with access to their own data, a future challenge for researchers and practitioners alike

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

Using Spectral Analysis to Evaluate Flute Tone Quality

Many skilled flutists place a high priority on good tone quality, or timbre. Timbre can be defined as the audible difference in character that a listener perceives for two notes played at the same pitch. Different timbres are determined by the combination and balance of harmonics that comprise a note. Unlike pitch and rhythm, timbre is difficult to objectively quantify. This project explores (1) how tone quality is described by skilled flutists, (2) whether the harmonic spectrum has some correlation with tone quality, (3) whether certain harmonic spectra are preferred, or considered good . Thirty-one flutists ranging from high school students to professionals were recorded. A set of samples was used in surveys and interviews to capture descriptors and ratings of tone quality. All of the recorded samples were analyzed using application programs, Harmonic Analysis Tools (HAT), created for this study. HAT uses digital signal processing techniques to produce spectral signatures . The signatures consist of the harmonic content, pitch, and amplitude of a sample. In the future, with further development, HAT may be a useful tool for musicians for tone development in the practice room. The outcome of this research is a baseline set of some often used descriptors. In addition, results showed some correlation between harmonic spectra and descriptors. There were also trends in preferences with respect to certain spectral characteristics. An unexpected finding was that University students showed divergent timbre preferences compared to highly experienced flutists

PlayRightNow - Designing a media player experience for PlayNow arena

This paper discusses the process of designing a media player tailored for PlayNowTM arena with the purpose of enhancing the user experience of this media portal. The design process is divided into two main stages, the first consisting on gathering information to inform the design of a media player and the second stage involving a low-fidelity prototype of a media player. In the first stage, three main activities are carried out to inform the design of the interface: a literary review of relevant research and studies related to the way people use digital media and its effect on society; an evaluation of the interfaces and features offered by some of the existing popular media players in the market today from an interaction design point of view; and user observations and interviews on people’s relation to digital media. Based on the information and data collected from the first stage, an iterative process of design of interfaces was adapted, whereby potential users and design experts were consulted with their opinions and suggestions that influenced the sketching of various possible interfaces. Finally, a design of a media player for PlayNowTM arena is proposed, which is believed to have the potential of providing its users with a better experience in relation to digital content, as well as attracting new customers and increasing the revenue of this media portal

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

Explanations in Music Recommender Systems in a Mobile Setting

Revised version: some spelling errors corrected.Every day, millions of users utilize their mobile phones to access music streaming services such as Spotify. However, these `black boxes’ seldom provide adequate explanations for their music recommendations. A systematic literature review revealed that there is a strong relationship between moods and music, and that explanations and interface design choices can effect how people perceive recommendations just as much as algorithm accuracy. However, little seems to be known about how to apply user-centric design approaches, which exploit affective information to present explanations, to mobile devices. In order to bridge these gaps, the work of Andjelkovic, Parra, & O’Donovan (2019) was extended upon and applied as non-interactive designs in a mobile setting. Three separate Amazon Mechanical Turk studies asked participants to compare the same three interface designs: baseline, textual, and visual (n=178). Each survey displayed a different playlist with either low, medium, or high music popularity. Results indicate that music familiarity may or may not influence the need for explanations, but explanations are important to users. Both explanatory designs fared equally better than the baseline, and the use of affective information may help systems become more efficient, transparent, trustworthy, and satisfactory. Overall, there does not seem to be a `one design fits all’ solution for explanations in a mobile setting.Master's Thesis in Information ScienceINFO390MASV-INFOMASV-IK

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc

Understanding and Supporting Directed Content Sharing on the Web

To find interesting, personally relevant web content, we often rely on friends and colleagues to pass links along as they encounter them. In this paper, we study and augment link-sharing via e-mail, the most popular means of sharing web content today. Armed with survey data indicating that active sharers of novel web content are often those that actively seek it out, we present FeedMe, a plug-in for Google Reader that makes directed sharing of content a more salient part of the user experience. Our survey research indicates that sharing is moderated by concern about relevancy to the recipient, a desire to send only novel content to the recipient, and the effort required to share. FeedMe allays these concerns by recommending friends who may be interested in seeing the content, providing information on what the recipient has seen and how many emails they have received recently, and giving recipients the opportunity to provide lightweight feedback when they appreciate shared content. FeedMe introduces a novel design space for mixed-initiative social recommenders: friends who know the user voluntarily vet the material on the userâ s behalf. We present a two week field experiment (N=60) demonstrating that FeedMeâ s recommendations and social awareness features made it easier and more enjoyable to share content that recipients appreciated and would not have found otherwise

Navigating the space of your music

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 121-124).Navigating increasingly large personal music libraries is commonplace. Yet most music browsers do not enable their users to explore their collections in a guided and manipulable fashion, often requiring them to have a specific target in mind. MusicBox is a new music browser that provides this interactive control by mapping a music collection into a two-dimensional space, applying principal components analysis (PCA) to a combination of contextual and content-based features of each of the musical tracks. The resulting map shows similar songs close together and dissimilar songs farther apart. MusicBox is fully interactive and highly flexible: users can add and remove features from the included feature list, with PCA recomputed on the fly to remap the data. MusicBox is also extensible; we invite other music researchers to contribute features to its PCA engine. A small user study has shown that MusicBox helps users to find music in their libraries, to discover new music, and to challenge their assumptions about relationships between types of music.by Anita Shen Lillie.S.M

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science