Evolution of global music trends: An exploratory and predictive approach based on Spotify data

Music listening choices are considered to be a factor capable of measuring people's emotions. Thanks to the explosion of streaming music applications in recent years, it is possible to describe listening trends of the global population based on emotional features. In this paper we have analysed the most popular songs from 52 countries on Spotify through their features of danceability, positivity and intensity. This analysis allows exploring how these song features reflect mood trends along with other contextual factors that may affect the population's listening behaviour, such as the weather or the influence of the COVID-19 pandemic. Finally, we have proposed a multivariate time series model to predict the preferred type of music in those countries based on their previous music listening patterns and the contextual factors. The results show some relevant behavioural changes in these patterns due to the effect of the pandemic. Furthermore, the resulting prediction model enables forecasting the type of music listened to in three different groups of countries in the next 4 months with an error around 1%. These results may help to better understand streaming music consumption in businesses related to the music and marketing industry. © 2022 Elsevier B.V.

Web-Based Networked Music Performances via WebRTC: A Low-Latency PCM Audio Solution

Nowadays, widely used videoconferencing software has been diffused even further by the social distancing measures adopted during the SARS-CoV-2 pandemic. However, none of the Web-based solutions currently available support high-fidelity stereo audio streaming, which is a fundamental prerequisite for networked music applications. This is mainly because of the fact that the WebRTC RTCPeerConnection standard or Web-based audio streaming do not handle uncompressed audio formats. To overcome that limitation, an implementation of 16-bit pulse code modulation (PCM) stereo audio transmission on top of the WebRTC RTCDataChannel, leveraging Web Audio and AudioWorklets, is discussed. Results obtained with multiple configurations, browsers, and operating systems show that the proposed approach outperforms the WebRTC RTCPeerConnection standard in terms of audio quality and latency, which in the authors' best case to date has been reduced to only 40 ms between two MacBooks on a local area network. © 2022 Audio Engineering Society. All rights reserved.