433 research outputs found
"More of an art than a science": Supporting the creation of playlists and mixes
This paper presents an analysis of how people construct playlists and mixes. Interviews with practitioners and postings made to a web site are analyzed using a grounded theory approach to extract themes and categorizations. The information sought is often encapsulated as music information retrieval tasks, albeit not as the traditional "known item search" paradigm. The collated data is analyzed and trends identified and discussed in relation to
music information retrieval algorithms that could help support such activity
Using Song Social Tags and Topic Models to Describe and Compare Playlists
Playlists are a natural delivery method for music recommendation and discovery systems. Recommender systems offering playlists must strive to make them relevant and enjoyable. In this paper we survey many current means of generating and evaluating playlists. We present a means of comparing playlists in a reduced dimensional space through the use of aggregated tag clouds and topic models. To evaluate the fitness of this measure, we perform prototypical retrieval tasks on playlists taken from radio station logs gathered from Radio Paradise and Yes.com, using tags from Last.fm with the result showing better than random performance when using the query playlist's station as ground truth, while failing to do so when using time of day as ground truth. We then discuss possible applications for this measurement technique as well as ways it might be improved
Developing a context-aware automatic playlist generator (CAAPG)
Thesis submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2014The current digitization of music and the sheer volume of the musical content
available to listeners on local devices, such as mobile phones and IPod has been
revolutionary. This trend has changed the way humans interact and experience
their music. Music listeners can listen to their songs on the move. The most recent
trend in the music industry is that users can organize and search for their songs
based on emotions. However, most users have to manually create their playlists for
particular situations. The work that this entails is cumbersome and sometimes
negates the experience of the listener. The intuitive response to this problem is
developing an automatic playlist generating (APG) system. Research on APG mostly
focuses on using traditional metadata and audio similarity methods to create a
playlist. In addition APG is seen as a static problem [1]. This means that APG is
seen as a problem that does not change, however music listeners are always
changing their listening habits.
This thesis supports and follows from the argument made in Chi chung-yiâs work -
that the APG problem is a continuous optimization problem. Additionally, in this
paper I also argue that the best way to give users a good listening experience is to
understand the userâs preference(s) depending on the context. Context here simply
mean the features that make up the environmental space in which the system is
being used. The main idea in this paper is to show the importance of emotional
categorization in the generation of playlist content, while simultaneously mapping those categories to the userâs context based on the users past activities on the
system.
Reinforcement learning is the method used in this thesis to generate a personalized
playlist, based on the context of use and the userâs emotional preference. After
implementing the system we use two hypothetical users to simulate the use of our system. Various metrics are defined to measure the performance of this approach.Ashesi University Colleg
Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning
According to the World Health Organization(WHO), it is estimated that
approximately 1.3 billion people live with some forms of vision impairment
globally, of whom 36 million are blind. Due to their disability, engaging these
minority into the society is a challenging problem. The recent rise of smart
mobile phones provides a new solution by enabling blind users' convenient
access to the information and service for understanding the world. Users with
vision impairment can adopt the screen reader embedded in the mobile operating
systems to read the content of each screen within the app, and use gestures to
interact with the phone. However, the prerequisite of using screen readers is
that developers have to add natural-language labels to the image-based
components when they are developing the app. Unfortunately, more than 77% apps
have issues of missing labels, according to our analysis of 10,408 Android
apps. Most of these issues are caused by developers' lack of awareness and
knowledge in considering the minority. And even if developers want to add the
labels to UI components, they may not come up with concise and clear
description as most of them are of no visual issues. To overcome these
challenges, we develop a deep-learning based model, called LabelDroid, to
automatically predict the labels of image-based buttons by learning from
large-scale commercial apps in Google Play. The experimental results show that
our model can make accurate predictions and the generated labels are of higher
quality than that from real Android developers.Comment: Accepted to 42nd International Conference on Software Engineerin
Track Co-occurrence Analysis of Users' Music Listening History
Music services provide listeners access to great numbers of available tracks. It is time consuming for listeners to find potential favorite ones. Music listeners increasingly want playlists to be created automatically. This study examines the relationship between background knowledge about music and track co-occurrence frequency in usersâ music listening history and builds a multiple linear regression model to predict the track co-occurrence. So given a seed track, the model can find out which track is most likely to co-occur. A simple objective evaluation compares predicted track with tracks in the usersâ listening history. 13 out of 15 test tracks find the highest rank predicted track in the same listening history.Master of Science in Information Scienc
Emotion, Content & Context in Sound and Music
Computer game sound is particularly dependent upon the use of both sound artefacts and music. Sound and music are media rich in information. Audio and music processing can be approached from a range of perspectives which may or may not consider the meaning and purpose of this information. Computer music and digital audio are being advanced through investigations into emotion, content analysis, and context, and this chapter attempts to highlight the value of considering the information content present in sound, the context of the user being exposed to the sound, and the emotional reactions and interactions that are possible between the user and game sound. We demonstrate that by analysing the information present within media and considering the applications and purpose of a particular type of information, developers can improve user experiences and reduce overheads while creating more suitable, efficient applications. Some illustrated examples of our research projects that employ these theories are provided. Although the examples of research and development applications are not always examples from computer game sound, they can be related back to computer games. We aim to stimulate the readerâs imagination and thought in these areas, rather than attempt to drive the reader down one particular path
Automatic music playlist generation using affective technologies
This paper discusses how human emotion could be quantified using contextual and physiological information that has been gathered from a range of sensors, and how this data could then be used to automatically generate music playlists. I begin by discussing existing affective systems that automatically generate playlists based on human emotion. I then consider the current work in audio description analysis. A system is proposed that measures human emotion based on contextual and physiological data using a range of sensors. The sensors discussed to invoke such contextual characteristics range from temperature and light to EDA (electro dermal activity) and ECG (electrocardiogram). The concluding section describes the progress achieved so far, which includes defining datasets using a conceptual design, microprocessor electronics and data acquisition using MatLab. Lastly, there is brief discussion of future plans to develop this research
Crowdsourcing Emotions in Music Domain
An important source of intelligence for music emotion recognition today comes from user-provided
community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags,
design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in
the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs
which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of
various crowdsourcing instruments providing examples from research works. We also share our own
experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two
music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that
they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs
than negative ones
ARTE: Automated Generation of Realistic Test Inputs for Web APIs
Automated test case generation for web APIs is a thriving research topic, where test cases are frequently derived from the API specification. However, this process is only partially automated since testers are usually obliged to manually set meaningful valid test inputs for each input parameter. In this article, we present ARTE, an approach for the automated extraction of realistic test data for web APIs from knowledge bases like DBpedia. Specifically, ARTE leverages the specification of the API parameters to automatically search for realistic test inputs using natural language processing, search-based, and knowledge extraction techniques. ARTE has been integrated into RESTest, an open-source testing framework for RESTful APIs, fully automating the test case generation process. Evaluation results on 140 operations from 48 real-world web APIs show that ARTE can efficiently generate realistic test inputs for 64.9% of the target parameters, outperforming the state-of-the-art approach SAIGEN (31.8%). More importantly, ARTE supported the generation of over twice as many valid API calls (57.3%) as random generation (20%) and SAIGEN (26%), leading to a higher failure detection capability and uncovering several real-world bugs. These results show the potential of ARTE for enhancing existing web API testing tools, achieving an unprecedented level of automationJunta de AndalucĂa APOLO (US-1264651)Junta de AndalucĂa EKIPMENT-PLUS (P18-FR-2895)Ministerio de Ciencia, InnovaciĂłn y Universidades RTI2018-101204-B-C21 (HORATIO)Ministerio de Ciencia, InnovaciĂłn y Universidades RED2018-102472-
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