Search Results (4)

In recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is not clear yet how they can be realistically integrated into the artistic practice of musicians and music practitioners. This paper aims to address this question via Prompt Audio Generation User Research Investigation (PAGURI), a user experience study where we leverage recent text-to-music developments to study how musicians and practitioners interact with these systems, evaluating their satisfaction levels. We developed an online tool through which users can generate music samples and/or apply recently proposed personalization techniques based on fine-tuning to allow the text-to-music model to generate sounds closer to their needs and preferences. Using semi-structured interviews, we analyzed different aspects related to how participants interacted with the proposed tool to understand the current effectiveness and limitations of text-to-music models in enhancing users’ creativity. Our research centers on user experiences to uncover insights that can guide the future development of TTM models and their role in AI-driven music creation. Additionally, they offered insightful perspectives on potential system improvements and their integration into their music practices. The results obtained through the study reveal the pros and cons of the use of TTMs for creative endeavors. Participants recognized the system’s creative potential and appreciated the usefulness of its personalization features. However, they also identified several challenges that must be addressed before TTMs are ready for real-world music creation, particularly issues of prompt ambiguity, limited controllability, and integration into existing workflows. Full article

Artificial intelligence and Internet of Things are playing an increasingly important role in monitoring beehives. In this paper, we propose a method for automatic recognition of honeybee type by analyzing the sound generated by worker bees and drone bees during their flight close to an entrance to a beehive. We conducted a wide comparative study to determine the most effective preprocessing of audio signals for the detection problem. We compared the results for several different methods for signal representation in the frequency domain, including mel-frequency cepstral coefficients (MFCCs), gammatone cepstral coefficients (GTCCs), the multiple signal classification method (MUSIC) and parametric estimation of power spectral density (PSD) by the Burg algorithm. The coefficients serve as inputs for an autoencoder neural network to discriminate drone bees from worker bees. The classification is based on the reconstruction error of the signal representations produced by the autoencoder. We propose a novel approach to class separation by the autoencoder neural network with various thresholds between decision areas, including the maximum likelihood threshold for the reconstruction error. By classifying real-life signals, we demonstrated that it is possible to differentiate drone bees and worker bees based solely on audio signals. The attained level of detection accuracy enables the creation of an efficient automatic system for beekeepers. Full article

Even though algorithmic composition might be considered a centuries-old concept, it has been gaining particular momentum since the introduction of computer-based techniques. The development of artificial intelligence (AI) methods, culminating in the latest achievements of deep learning techniques, has provided tools to automatically compose and even produce music. This paper discusses various aspects of the entire process within a context of designing a system able to automatically generate a score and recordings belonging to selected musical genres. It begins with the idea and design overview, followed by considerations regarding the algorithmic formulation of selected musical rules and principles. The system implements a hybrid approach, combining conventional, i.e., stochastic or rule-based, and AI elements. The latter are applied to facilitate the generation of selected layers of composition and to constitute a classifier with a task of evaluating the generated recordings. Selected stages of music generation are discussed, for example how motifs are processed into phrases and how phrases are used in the context of a whole song. To validate the system operation results, an evaluation of the quality of the produced music recordings was conducted, including a test with a group of listeners. The analysis also touches upon some legal aspects related to the creation of algorithmic compositions. Full article

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers. Full article