People-Powered Music: Using User-Generated Tags and Structure in Recommendations

Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches

A Systematic Comparison of Music Similarity Adaptation Approaches

In order to support individual user perspectives and different retrieval tasks, music similarity can no longer be considered as a static element of Music Information Retrieval (MIR) systems. Various approaches have been proposed recently that allow dynamic adaptation of music similarity measures. This paper provides a systematic comparison of algorithms for metric learning and higher-level facet distance weighting on the MagnaTagATune dataset. A crossvalidation variant taking into account clip availability is presented. Applied on user generated similarity data, its effect on adaptation performance is analyzed. Special attention is paid to the amount of training data necessary for making similarity predictions on unknown data, the number of model parameters and the amount of information available about the music itself. 1

A statistical analysis of the ABC music notation corpus: exploring duplication

This paper presents a statistical analysis of the abc music notation corpus. The corpus contains around 435,000 transcriptions of which just over 400,000 are folk and traditional music. There is significant duplication within the corpus and so a large part of the paper discusses methods to assess the level of duplication and the analysis then indicates a headline figure of over 165,000 distinct folk and traditional melodies. The paper also describes TuneGraph, an online, interactive user interface for exploring tune variants, based on visualising the proximity graph of the underlying melodies

Discovering Communication

What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication

Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis

One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology- and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost

TuneGraph, an online visual tool for exploring melodic similarity

This paper presents TuneGraph, an online visual tool for exploring melodic similarity. The underlying data comes from a large index of online music, all transcribed in abc notation, and TuneGraph uses a melodic similarity metric to derive a proximity graph representing similarities within the index. A rich but dense graph is built and then sparsfied weak, non-essential edges. From this a local graph is extracted for each vertex, aimed at indicating close variants of, and similar melodies to, the underlying tune represented by the vertex. Finally an interactive user interface displays each local graph on that tune's webpage, allowing the user to explore melodically similar tunes

Investigating keyframe selection methods in the novel domain of passively captured visual lifelogs

The SenseCam is a passive capture wearable camera, worn around the neck, and when worn continuously it takes an average of 1,900 images per day. It can be used to create a personal lifelog or visual recording of the wearer’s life which can be helpful as an aid to human memory. For such a large amount of visual information to be useful, it needs to be structured into “events”, which can be achieved through automatic segmentation. An important component of this structuring process is the selection of keyframes to represent individual events. This work investigates a variety of techniques for the selection of a single representative keyframe image from each event, in order to provide the user with an instant visual summary of that event. In our experiments we use a large test set of 2,232 lifelog events collected by 5 users over a time period of one month each. We propose a novel keyframe selection technique which seeks to select the image with the highest “quality” as the keyframe. The inclusion of “quality” approaches in keyframe selection is demonstrated to be useful owing to the high variability in image visual quality within passively captured image collections

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges