Recent advances in generative AI have made music generation a prominent research focus. However, many neural-based models rely on large datasets, raising concerns about copyright infringement and high-performance costs. In contrast, we propose MusicAIR, an innovative multimodal AI music generation framework powered by a novel algorithm-driven symbolic music core, effectively mitigating copyright infringement risks. The music core algorithms connect critical lyrical and rhythmic information to automatically derive musical features, creating a complete, coherent melodic score solely from the lyrics. The MusicAIR framework facilitates music generation from lyrics, text, and images. The generated score adheres to established principles of music theory, lyrical structure, and rhythmic conventions. We developed Generate AI Music (GenAIM), a web tool using MusicAIR for lyric-to-song, text-to-music, and image-to-music generation. In our experiments, we evaluated AI-generated music scores produced by the system using both standard music metrics and innovative analysis that compares these compositions with original works. The system achieves an average key confidence of 85%, outperforming human composers at 79%, and aligns closely with established music theory standards, demonstrating its ability to generate diverse, human-like compositions. As a co-pilot tool, GenAIM can serve as a reliable music composition assistant and a possible educational composition tutor while simultaneously lowering the entry barrier for all aspiring musicians, which is innovative and significantly contributes to AI for music generation.

Select YouTube creators can now produce their own remixes of existing songs. YouTube has announced a new feature for its AI-powered Dream Track tool that allows individuals to "restyle" a song and create a 30-second tune to use in a Short. Creators in the experiment group for this feature can choose from eligible songs and then give an explanation to AI about how they want to remix it. These changes could focus on giving the song a different genre or mood -- whatever twist they're imagining. From there a new song gets produced "that reimagines the music while maintaining the essence of the original song's vocals and lyrics," YouTube's announcement states.

This study presents a machine learning framework for assessing similarity between audio content and predicting sentiment score. We construct a dataset containing audio samples from music covers on YouTube along with the audio of the original song, and sentiment scores derived from user comments, serving as proxy labels for content quality. Our approach involves extensive pre-processing, segmenting audio signals into 30-second windows, and extracting high-dimensional feature representations through Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Temporal characteristics. Leveraging these features, we train regression models to predict sentiment scores on a 0-100 scale, achieving root mean square error (RMSE) values of 3.420, 5.482, 2.783, and 4.212, respectively. Improvements over a baseline model based on absolute difference metrics are observed. These results demonstrate the potential of machine learning to capture sentiment and similarity in audio, offering an adaptable framework for AI applications in media analysis.

Cover songs are alternate versions of a song by a different artist. Long being a vital part of the music industry, cover songs significantly influence music culture and are commonly heard in public venues. The rise of online music platforms has further increased their prevalence, often as background music or video soundtracks. While current automatic identification methods serve adequately for original songs, they are less effective with cover songs, primarily because cover versions often significantly deviate from the original compositions. In this paper, we propose a novel method for cover song detection that utilizes the lyrics of a song. We introduce a new dataset for cover songs and their corresponding originals. The dataset contains 5078 cover songs and 2828 original songs. In contrast to other cover song datasets, it contains the annotated lyrics for the original song and the cover song. We evaluate our method on this dataset and compare it with multiple baseline approaches. Our results show that our method outperforms the baseline approaches.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. With artificial intelligence seemingly infiltrating every facet of our lives, one church decided to experiment with the technology for one of its services last week. The Violet Crown City Church, located in Austin, held an AI-generated service on Sunday, describing the experiment as "uncharted territory." "This Sunday we're entering somewhat uncharted territory by letting ChatGPT create the order of worship, prayers, sermon, liturgy, and even an original song for our 10 a.m. ChatGPT logo and AI Artificial Intelligence words are seen in this illustration taken, May 4, 2023. "The purpose is to invite us to consider the nature of truth and challenge our assumptions about what God can make sacred and inspired." The church acknowledged such an experiment would be easy to write off, but encouraged its members to keep an open mind. "[W]hy not attend instead and experience it for yourself?" the church said, clarifying that this would be a "one-time experiment and not something we'll likely do again." The church assuaged any worries that "Skynet" – a reference to the fictional AI system in the Terminator franchise – had taken control of the church. One church attendee told KXAN he was able to worship, but the service ultimately lacked the human touch. "I'm not sure that AI can actually express the emotions of love and kindness and empathy," Chambers said. "I think that we must practice love and express that.

Piano covers of pop music are enjoyed by many people. However, the task of automatically generating piano covers of pop music is still understudied. This is partly due to the lack of synchronized {Pop, Piano Cover} data pairs, which made it challenging to apply the latest data-intensive deep learning-based methods. To leverage the power of the data-driven approach, we make a large amount of paired and synchronized {Pop, Piano Cover} data using an automated pipeline. In this paper, we present Pop2Piano, a Transformer network that generates piano covers given waveforms of pop music. To the best of our knowledge, this is the first model to generate a piano cover directly from pop audio without using melody and chord extraction modules. We show that Pop2Piano, trained with our dataset, is capable of producing plausible piano covers.

Recent years have seen an increasing number of articles that speculate about the possibility, even certainty, that artificial intelligence (AI) will some day produce musical works comparable to those done by human artists. In fact, it may already be happening behind our eyes without us realizing it. An example is machine-generated TV programming. Automated software algorithm programs follow patterns we've come to expect from television dramas. We know what we want out of a good plotline or episode -- things that keep us coming back for more.

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

Tech innovator and singer Prof Maya Ackerman sees AI as the perfect testing ground for music, where people's creativity can really flourish. While there are many facets of artificial intelligence (AI) that seem destined to takeover our lives, there seems to be fewer pursuits destined to become filled with robots than music. With the meteoric rise of music streaming and its ability to track our music interests, likes and dislikes, music producers have as good a picture as ever of what to make that has a high-percentage chance of topping the charts. However, away from the business end, some researchers and artists are finding ways to use machine learning to create a human/robot collaboration that few would discern is based on an algorithm. One such individual is Prof Maya Ackerman, a leading AI researcher based at the computer engineering department at Santa Clara University in the US.

Grace Vanderwaal recently bonded with professional juggler Viktor Kee backstage in "America's Got Talent" Season 11. On her Instagram account, the 12-year-old singer shared a face swap video with her fellow contestant. In the clip, Vanderwaal, surprised by how Kee looked, said that he looks like a baby with her face and Kee's bald head. The "I Don't Know My Name" singer then joked that she hopes her followers on Instagram wouldn't get nightmares after seeing their video. Vanderwaal also shared another video with Kee who taught her some of his juggling tricks.