In this paper, we study the effect of popularity degradation bias in the context of local music recommendations. Specifically, we examine how accurate two top-performing recommendation algorithms, Weight Relevance Matrix Factorization (WRMF) and Multinomial Variational Autoencoder (Mult-VAE), are at recommending artists as a function of artist popularity. We find that both algorithms improve recommendation performance for more popular artists and, as such, exhibit popularity degradation bias. While both algorithms produce a similar level of performance for more popular artists, Mult-VAE shows better relative performance for less popular artists. This suggests that this algorithm should be preferred for local (long-tail) music artist recommendation.

Artificial Intelligence (AI) technologies such as deep learning are evolving very quickly bringing many changes to our everyday lives. To explore the future impact and potential of AI in the field of music and sound technologies a doctoral day was held between Queen Mary University of London (QMUL, UK) and Sciences et Technologies de la Musique et du Son (STMS, France). Prompt questions about current trends in AI and music were generated by academics from QMUL and STMS. Students from the two institutions then debated these questions. This report presents a summary of the student debates on the topics of: Data, Impact, and the Environment; Responsible Innovation and Creative Practice; Creativity and Bias; and From Tools to the Singularity. The students represent the future generation of AI and music researchers. The academics represent the incumbent establishment. The student debates reported here capture visions, dreams, concerns, uncertainties, and contentious issues for the future of AI and music as the establishment is rightfully challenged by the next generation.

Classifying research output into context-specific label taxonomies is a challenging and relevant downstream task, given the volume of existing and newly published articles. We propose a method to enhance the performance of article classification by enriching simple Graph Neural Networks (GNN) pipelines with edge-heterogeneous graph representations. SciBERT is used for node feature generation to capture higher-order semantics within the articles' textual metadata. Fully supervised transductive node classification experiments are conducted on the Open Graph Benchmark (OGB) ogbn-arxiv dataset and the PubMed diabetes dataset, augmented with additional metadata from Microsoft Academic Graph (MAG) and PubMed Central, respectively. The results demonstrate that edge-heterogeneous graphs consistently improve the performance of all GNN models compared to the edge-homogeneous graphs. The transformed data enable simple and shallow GNN pipelines to achieve results on par with more complex architectures. On ogbn-arxiv, we achieve a top-15 result in the OGB competition with a 2-layer GCN (accuracy 74.61%), being the highest-scoring solution with sub-1 million parameters. On PubMed, we closely trail SOTA GNN architectures using a 2-layer GraphSAGE by including additional co-authorship edges in the graph (accuracy 89.88%). The implementation is available at: $\href{https://github.com/lyvykhang/edgehetero-nodeproppred}{\text{https://github.com/lyvykhang/edgehetero-nodeproppred}}$.

In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting. Motivated by recent advances in the computer vision domain, we are the first to explore the combination of pre-trained text-to-audio diffusers with two established personalization methods. We experiment with the effect of audio-specific data augmentation on the overall system performance and assess different training strategies. For evaluation, we construct a novel dataset with prompts and music clips. We consider both embedding-based and music-specific metrics for quantitative evaluation, as well as a user study for qualitative evaluation. Our analysis shows that similarity metrics are in accordance with user preferences and that current personalization approaches tend to learn rhythmic music constructs more easily than melody. The code, dataset, and example material of this study are open to the research community.

The proliferation of fake news has become a significant concern in recent times due to its potential to spread misinformation and manipulate public opinion. This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese, focusing on journalistic-type news. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec, to extract features from textual data. We evaluate the performance of various classification algorithms, such as logistic regression, support vector machine, random forest, AdaBoost, and LightGBM, on a dataset containing both true and fake news articles. The proposed approach achieves high accuracy and F1-Score, demonstrating its effectiveness in identifying fake news. Additionally, we developed a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity. Our platform provides real-time analysis, allowing users to assess the likelihood of fake news articles. Through empirical analysis and comparative studies, we demonstrate the potential of our approach to contribute to the fight against the spread of fake news and promote more informed media consumption.

Edwards, who ended up using the real, flesh-and-blood human Hans Zimmer for the soundtrack of his movie, said he played the AI-generated track back to the composer. Zimmer, he said, found it amusing. Edwards's experiment speaks to an issue at the heart of one of the biggest fights facing Hollywood today. Artists and creatives are up in arms over generative AI. Hollywood is currently at a standstill as actors and writers are striking over fairer labor conditions and the use of generative AI in the film industry.

Alison Lomax's presence on the video streaming platform she runs is relatively scant compared with the YouTubers with whom she spends much of her time. But what clips exist succinctly chart the marketing tech revolution she's been navigating: there's a badly framed 12 minutes from 2014 of Lomax lecturing on the rise of influencers working with brands; in another she describes how TV companies woke up to the potential of partnering with YouTube in 2016; and there's her on stage at London's podcast show this year, discussing YouTube's imminent relaunch into the booming audio format. Now, Lomax stands at the "inflection point" of the next hot technology: the generative artificial intelligence behind chatbots such as ChatGPT and image generators such as MidJourney. YouTube, launched in 2005, is no stranger to AI: it is used in its recommendation algorithm; to moderate content; and, latterly, for automatic language translation. "We're committed to embracing AI in a bold way," says Lomax. "But we have to do it really responsibly."

These streamers are not real: they are AI-generated clones of the real streamers. As technologies that create realistic avatars, voices, and movements get more sophisticated and affordable, the popularity of these deepfakes has exploded across China's e-commerce streaming platforms. Today, livestreaming is the dominant marketing channel for traditional and digital brands in China. The top names can sell more than a billion dollars' worth of goods in one night and gain royalty status just like big movie stars. But at the same time, training livestream hosts, retaining them, and figuring out the technical details of broadcasting comes with a significant cost for smaller brands. Since 2022, a swarm of Chinese startups and major tech companies have been offering the service of creating deepfake avatars for e-commerce livestreaming.

Every night, I bury my tinnitus under brown noise, cats' purrs, and the tap of rain on leaves. This "functional music," as it is known, certainly serves its function--lulling me to sleep. It also accounts for a vast portion of the streaming world: Spotify recently revealed that white noise and ambient podcasts rack up 3 million hours of listens a day. In a weird industry quirk, these ambient sounds, often recorded or generated by AI, are assigned the same monetary value as actual songs. One stream is one credit--one equal piece of the pot that's shared among everyone.

Understanding and mitigating political bias in online social media platforms are crucial tasks to combat misinformation and echo chamber effects. However, characterizing political bias temporally using computational methods presents challenges due to the high frequency of noise in social media datasets. While existing research has explored various approaches to political bias characterization, the ability to forecast political bias and anticipate how political conversations might evolve in the near future has not been extensively studied. In this paper, we propose a heuristic approach to classify social media posts into five distinct political leaning categories. Since there is a lack of prior work on forecasting political bias, we conduct an in-depth analysis of existing baseline models to identify which model best fits to forecast political leaning time series. Our approach involves utilizing existing time series forecasting models on two social media datasets with different political ideologies, specifically Twitter and Gab. Through our experiments and analyses, we seek to shed light on the challenges and opportunities in forecasting political bias in social media platforms. Ultimately, our work aims to pave the way for developing more effective strategies to mitigate the negative impact of political bias in the digital realm.