Claims that generative AI enhances human creativity rely on individualistic notions that obscure the socio-technical conditions shaping creative practice. This framing enables what we call creativity-washing—the mobilisation of creativity discourse to legitimise corporate control over creative labour—and narrows evaluation to copyright compliance and user experience. We propose a multi-relational evaluative framework examining generative AI across scales: micro-level experience (postphenomenology), meso-level networks of agency (actor-network theory), and macro-level cultural and economic conditions (materialist-relational perspective). The framework surfaces concerns that single-dimension metrics, such as openness rankings, usability studies, and output benchmarks, render invisible. We illustrate the framework through a comparative analysis of two generative audio systems, Stable Audio Open and RAVE. The analysis yields findings at each scale, including the observation that openness at the point of access can coexist with enclosure at the point of governance.

Navigating large audio collections presents a significant challenge due to the intricate nature of sound properties and the varied needs of users. To enhance user experience, audio-sharing platforms offer a sound similarity function, which leverages vector-based representations of audio clips to facilitate the retrieval of sounds. This study evaluates the retrieval performances of one manually engineered audio representation and thirteen deep audio embeddings in the semantic sound similarity task. By employing a diverse range of models, our research investigates the effects of utilizing different input modalities and training objectives. In the process, we explore various design choices for integrating embeddings into sound similarity systems. Our evaluation is based on objective ranked performance metrics that incorporate sound classes and sound families, complemented by preliminary subjective assessments. We observe that the multimodal models using audio and language modalities outperform audio-only models by a significant margin, which in turn outperform audio and image models. Notably, the state-of-the-art models on the sound event classification task are not the topperforming models on the semantic sound similarity task. In addition, our findings in embedding processing methods and similarity search functions provide insights broadly applicable to information retrieval systems across different modalities.

Current sound-based practices and systems developed in both academia and industry point to convergent research trends that bring together the field of Sound and Music Computing with that of the Internet of Things. This paper proposes a vision for the emerging field of the Internet of Sounds (IoS), which stems from such disciplines. The IoS relates to the network of Sound Things, i.e., devices capable of sensing, acquiring, processing, actuating, and exchanging data serving the purpose of communicating sound-related information. In the IoS paradigm, which merges under a unique umbrella the emerging fields of the Internet of Musical Things and the Internet of Audio Things, heterogeneous devices dedicated to musical and non-musical tasks can interact and cooperate with one another and with other things connected to the Internet to facilitate sound-based services and applications that are globally available to the users. We survey the state of the art in this space, discuss the technological and non-technological challenges ahead of us and propose a comprehensive research agenda for the field.

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and encompassing over 500 sound classes. However, AudioSet is not an open dataset as its official release consists of pre-computed audio features. Downloading the original audio tracks can be problematic due to YouTube videos gradually disappearing and usage rights issues. To provide an alternative benchmark dataset and thus foster SER research, we introduceFSD50K, an open dataset containing over 51 k audio clips totalling over 100 h of audio manually labeled using 200 classes drawn from the AudioSet Ontology. The audio clips are licensed under Creative Commons licenses, making the dataset freely distributable (including waveforms). We provide a detailed description of the FSD50K creation process, tailored to the particularities of Freesound data, including challenges encountered and solutions adopted. We include a comprehensive dataset characterization along with discussion of limitations and key factors to allow its audio-informed usage. Finally, we conduct sound event classification experiments to provide baseline systems as well as insight on the main factors to consider when splitting Freesound audio data for SER. Our goal is to develop a dataset to be widely adopted by the community as a new open benchmark for SER research.

The large size of nowadays' online multimedia databases makes retrieving their content a difficult and time-consuming task. Users of online sound collections typically submit search queries that express a broad intent, often making the system return large and unmanageable result sets. Search Result Clustering is a technique that organises search-result content into coherent groups, which allows users to identify useful subsets in their results. Obtaining coherent and distinctive clusters that can be explored with a suitable interface is crucial for making this technique a useful complement of traditional search engines. In our work, we propose a graph-based approach using audio features for clustering diverse sound collections obtained when querying large online databases. We propose an approach to assess the performance of different features at scale, by taking advantage of the metadata associated with each sound. This analysis is complemented with an evaluation using ground-truth labels from manually annotated datasets. We show that using a confidence measure for discarding inconsistent clusters improves the quality of the partitions. After identifying the most appropriate features for clustering, we conduct an experiment with users performing a sound design task, in order to evaluate our approach and its user interface. A qualitative analysis is carried out including usability questionnaires and semi-structured interviews. This provides us with valuable new insights regarding the features that promote efficient interaction with the clusters.

As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42.5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noise-robust loss functions can be effective in improving performance in presence of corrupted labels.

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 sound's 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.

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.

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.

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.