Autonomous Network enablers for a 5G Network-on-Wheels are investigated, with a field-trial for a Communication Service Provider. The objective was to devise solutions to reduce time spent on network life cycle tasks, such as network function validation, system commission and service deployment. The field trial revealed significant data collection and analysis challenges even with a single cell campaign, this suggests severe costs with public-network scales. The work contributes a new architecture for experiments and optimisation of observability strategies with algorithms in 5G networks. This work helps operators more effectively denlov 5G networks and services.

Controllable neural audio synthesis of sound effects is a challenging task due to the potential scarcity and spectro-temporal variance of the data. Differentiable digital signal processing (DDSP) synthesisers have been successfully employed to model and control musical and harmonic signals using relatively limited data and computational resources. Here we propose NoiseBandNet, an architecture capable of synthesising and controlling sound effects by filtering white noise through a filterbank, thus going further than previous systems that make assumptions about the harmonic nature of sounds. We evaluate our approach via a series of experiments, modelling footsteps, thunderstorm, pottery, knocking, and metal sound effects. Comparing NoiseBandNet audio reconstruction capabilities to four variants of the DDSP-filtered noise synthesiser, NoiseBandNet scores higher in nine out of ten evaluation categories, establishing a flexible DDSP method for generating time-varying, inharmonic sound effects of arbitrary length with both good time and frequency resolution. Finally, we introduce some potential creative uses of NoiseBandNet, by generating variations, performing loudness transfer, and by training it on user-defined control curves.

Artificially intelligent music technology (AIMT) is a promising field with great potential for creating innovation in music. However, the considerations and concerns surrounding AI-generated music from the perspective of professional video game composers have yet to be fully explored. In this study, 11 professional video game composers were interviewed to determine how they feel about AIMT and how this informs future research and tool design within the games industry. The interviews were analyzed using a reflexive thematic analysis (RTA) to identify key themes. The study found that while composers recognize the benefits of music AI, they have complex concerns beyond the obvious concerns for AI infringing on their agency and creativity. There is an inherent clash between the creative ego and music AI, which can make it difficult for composers to embrace this technology. Furthermore, a lack of standard technical knowledge, support, understanding, and trust of music AI is impeding tool use within the industry. These findings have implications for music AI researchers and industry practitioners. By better understanding the concerns and considerations of professional creatives, researchers can design and communicate their tools more effectively to music professionals. Moreover, this study lays the foundation for empirical research into the relationship between professional creatives and emerging AI technology – a topic that is under-emphasised in current research.

While centred on the medium of audio, podcasts are often a multimedia concern, and one that has become hugely popular in recent years, though relatively little is known about the perspectives of podcast creators and their visions of innovation. This article details the results of an exploratory study conducted to enhance our understanding of potential innovation in the field of podcasting. Sixteen podcast creators were interviewed about their work and what they wanted from next-generation podcasts, in order to understand the requirements and expectations of tools that could be built to create new forms of audio-based programming. Through a combination of qualitative and quantitative analysis, we reveal novel findings, such as the duality between “listener-centric” and “creator-centric” innovations, to improve listener experience but also to unleash new creative possibilities in a streamlined production workflow. We shed light on what podcast creators envision as “next-generation podcasting,” the archetypal podcast production workflow, and creators’ expectations of podcasting tools. Combining these findings, we identify how the workflow could be modified to include new steps that will help to realise podcast creators’ visions. This study crystalises on important information about podcasters—their behavior and perspectives on the future of the medium—which will allow further research, design, and development in the field to be founded upon empirical observations.

Abstract Deep learning methods are recognised as state-of-the-art for many applications of machine learning. Recently, deep learning methods have emerged as a solution to the task of automatic music generation (AMG) using symbolic tokens in a target style, but their superiority over non-deep learning methods has not been demonstrated. Here, we conduct a listening study to comparatively evaluate several music generation systems along six musical dimensions: stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm. A range of models, both deep learning algorithms and other methods, are used to generate 30-s excerpts in the style of Classical string quartets and classical piano improvisations. Fifty participants with relatively high musical knowledge rate unlabelled samples of computer-generated and human-composed excerpts for the six musical dimensions. We use non-parametric Bayesian hypothesis testing to interpret the results, allowing the possibility of finding meaningfulnon-differences between systems’ performance. We find that the strongest deep learning method, a reimplemented version of Music Transformer, has equivalent performance to a non-deep learning method, MAIA Markov, demonstrating that to date, deep learning does not outperform other methods for AMG. We also find there still remains a significant gap between any algorithmic method and human-composed excerpts.

The creation, consumption and commercialisation of podcasts have increased rapidly in recent years, yet there is limited research exploring the creators who are often the source of the products in this relatively new medium, as well as the workflows they utilise in making podcasts. Based on semi-structured interviews with sixteen professional podcast creators, and subsequent thematic analysis, this paper 1) codifies and quantifies the activities involved in podcast creation, 2) distils the archetypal podcast production workflow, 3) finds that this workflow is remarkably consistent as a function of podcast genre and creator affiliation (independent or part of a media organisation), and 4) sheds light on the “creator” role that has become a distinctive feature of the medium. This snapshot of the inner workings of the creation process, in the evolution of a highly engaging medium, could form the basis for identifying potential innovations that would increase the interactivity of podcasts in future.