Frederic Font

Freesound technical demo

Freesound is an online collaborative sound database where people with diverse interests share recorded sound samples under Creative Commons licenses. It was started in 2005 and it is being maintained to support diverse research projects and as a service to the overall research and artistic community. In this demo we want to introduce Freesound to the multimedia community and show its potential as a research resource. We begin by describing some general aspects of Freesound, its architecture and functionalities, and then explain potential usages that this framework has for research applications.

Class-based tag recommendation and user-based evaluation in online audio clip sharing

Online sharing platforms often rely on collaborative tagging systems for annotating content. In this way, users themselves annotate and describe the shared contents using textual labels, commonly called tags. These annotations typically suffer from a number of issues such as tag scarcity or ambiguous labelling. Hence, to minimise some of these issues, tag recommendation systems can be employed to suggest potentially relevant tags during the annotation process. In this work, we present a tag recommendation system and evaluate it in the context of an online platform for audio clip sharing. By exploiting domain-specific knowledge, the system we present is able to classify an audio clip among a number of predefined audio classes and to produce specific tag recommendations for the different classes. We perform an in-depth user-based evaluation of the recommendation method along with two baselines and a former version that we described in previous work. This user-based evaluation is further complemented with a prediction-based evaluation following standard information retrieval methodologies. Results show that the proposed tag recommendation method brings a statistically significant improvement over the previous method and the baselines. In addition, we report a number of findings based on the detailed analysis of user feedback provided during the evaluation process. The considered methods, when applied to real-world collaborative tagging systems, should serve the purpose of consolidating the tagging vocabulary and improving the quality of content annotations.

Folksonomy-Based Tag Recommendation for Collaborative Tagging Systems

Collaborative tagging has emerged as a common solution for labelling and organising online digital content. However, collaborative tagging systems typically suffer from a number of issues such as tag scarcity or ambiguous labelling. As a result, the organisation and browsing of tagged content is far from being optimal. In this work the authors present a general scheme for building a folksonomy-based tag recommendation system to help users tagging online content resources. Based on this general scheme, the authorse describe eight tag recommendation methods and extensively evaluate them with data coming from two real-world large-scale datasets of tagged images and sound clips. Their results show that the proposed methods can effectively recommend relevant tags, given a set of input tags and tag co-occurrence information. Moreover, the authors show how novel strategies for selecting the appropriate number of tags to be recommended can significantly improve methods performances. Approaches such as the one presented here can be useful to obtain more comprehensive and coherent descriptions of tagged resources, thus allowing a better organisation, browsing and reuse of online content. Moreover, they can increase the value of folksonomies as reliable sources for knowledge-mining.

Analysis of the Impact of a Tag Recommendation System in a Real-World Folksonomy

Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis.

Small world networks and creativity in audio clip sharing

Sharing communities are changing the way audio clips are obtained in several areas, ranging from music to game design. The motivations for people to record and upload sounds to these sites are likely to be related to social factors. In this paper, we describe several networks that can be extracted from user activities in these systems. We propose the notion of creativity as an objective for this kind of community, and how some indicators of creativity can be extracted. We investigate the relationship between the network properties and the creative outcome indicators through an empirical analysis of data from Freesound.org, a widely used website for sharing audio clips.

Extending sound sample descriptions through the extraction of community knowledge

Sound and music online services driven by communities of users are filled with large amounts of user-created content that has to be properly described. In these services, typical sound and music modeling is performed using either content-based or context-based strategies, but no special emphasis is given to the extraction of knowledge from the community. We outline a research plan in the context of Freesound.org and propose ideas about how audio clip sharing sites could adapt and take advantage of particular user communities to improve the descriptions of their content.

Sound Sharing and Retrieval

Multimedia sharing has experienced an enormous growth in recent years, and sound sharing has not been an exception. Nowadays one can find online sound sharing sites in which users can search, browse, and contribute large amounts of audio content such as sound effects, field and urban recordings, music tracks, and music samples. This poses many challenges to enable search, discovery, and ultimately reuse of this content. In this chapter we give an overview of different ways to approach such challenges. We describe how to build an audio database by outlining different aspects to be taken into account. We discuss metadata-based descriptions of audio content and different searching and browsing techniques that can be used to navigate the database. In addition to metadata, we show sound retrieval techniques based on the extraction of audio features from (possibly) unannotated audio. We end the chapter by discussing advanced approaches to sound retrieval and by drawing some conclusions about present and future of sound sharing and retrieval. In addition to our explanations, we provide code examples that illustrate some of the concepts discussed.

Improving Audio Retrieval through Loudness Profile Categorization

The increasing popularity of audio content sharing in online platforms requires the development of techniques to better organize and retrieve this data. In this paper we look at how to improve similarity search through content categorization in the context of Freesound, a popular online sound sharing site. We focus on organization based on morphological description. In particular, we propose to improve search results by incorporating information about query sounds loudness profile. This is performed within a thresholding based framework and can be generalized to structure information about the temporal evolution of other sound attributes. We perform a subjective evaluation to demonstrate the practical relevance of our method.