The birth of ChatGPT brought a collection of anxieties regarding how large language models allow users to quickly subvert processes that once required human time, effort, passion, and understanding. And further, the tech sector's often stormy relationship with regulation and ethical oversight have left many fearful for a future where artificial intelligence replaces humans at work and stymies human creativity. While much of this alarm is well founded, we should also consider the possibility that human creativity can blossom in the age of AI. In 2025, we will start to see this manifest in our collective cultural response to technology. To examine how culture and creativity might adapt to the age of AI, we'll use hip-hop as an example.

Real-time emotion-based music arrangement, which aims to transform a given music piece into another one that evokes specific emotional resonance with the user in real-time, holds significant application value in various scenarios, e.g., music therapy, video game soundtracks, and movie scores. However, balancing emotion real-time fit with soft emotion transition is a challenge due to the fine-grained and mutable nature of the target emotion. Existing studies mainly focus on achieving emotion real-time fit, while the issue of soft transition remains understudied, affecting the overall emotional coherence of the music. In this paper, we propose SongDriver2 to address this balance. Specifically, we first recognize the last timestep's music emotion and then fuse it with the current timestep's target input emotion. The fused emotion then serves as the guidance for SongDriver2 to generate the upcoming music based on the input melody data. To adjust music similarity and emotion real-time fit flexibly, we downsample the original melody and feed it into the generation model. Furthermore, we design four music theory features to leverage domain knowledge to enhance emotion information and employ semi-supervised learning to mitigate the subjective bias introduced by manual dataset annotation. According to the evaluation results, SongDriver2 surpasses the state-of-the-art methods in both objective and subjective metrics. These results demonstrate that SongDriver2 achieves real-time fit and soft transitions simultaneously, enhancing the coherence of the generated music.

We present a novel music generation framework for music infilling, with a user friendly interface. Infilling refers to the task of generating musical sections given the surrounding multi-track music. The proposed transformer-based framework is extensible for new control tokens as the added music control tokens such as tonal tension per bar and track polyphony level in this work. We explore the effects of including several musically meaningful control tokens, and evaluate the results using objective metrics related to pitch and rhythm. Our results demonstrate that adding additional control tokens helps to generate music with stronger stylistic similarities to the original music. It also provides the user with more control to change properties like the music texture and tonal tension in each bar compared to previous research which only provided control for track density. We present the model in a Google Colab notebook to enable interactive generation.

In 2021, technology's role in how art is generated remains up for debate and discovery. From the rise of NFTs to the proliferation of techno-artists who use generative adversarial networks to produce visual expressions, to smartphone apps that write new music, creatives and technologists are continually experimenting with how art is produced, consumed, and monetized. BT, the Grammy-nominated composer of 2010's These Hopeful Machines, has emerged as a world leader at the intersection of tech and music. Beyond producing and writing for the likes of David Bowie, Death Cab for Cutie, Madonna, and the Roots, and composing scores for The Fast and the Furious, Smallville, and many other shows and movies, he's helped pioneer production techniques like stutter editing and granular synthesis. This past spring, BT released GENESIS.JSON, a piece of software that contains 24 hours of original music and visual art.

"And of course there was nothing more repellent than the synthesizer," said Morrissey, front man for the Smiths, in a 1983 interview, reflecting the arguments of the day that raged around whether the new electronic instruments of the 1970s qualified as "proper" music. In 1982, a branch of the U.K.'s Musician's Union even tried to ban the use of synths, on the grounds that they were taking work away from musicians who played stringed instruments. Those kinds of arguments may have a parallel today, in 2019, with the emergence of music created with artificial intelligence. Some of the questions (Is this "real" music? Can it compete with human-made melodies? If so, will it put those humans out of a job?) are eerily similar.

Royal Caribbean International has rolled out a new digital tool that makes an instant video montage of your vacation photographs and sets it to original music based on the content of the images. SoundSeeker uses artificial intelligence technology to scan your uploaded images and make the musical choices.

As artificial intelligence (AI) is applied to the creative arts, the first samples of computer-created music are emerging. In mid-2016, Google announced its AI Project Magenta to create music and art. Then, last May at Techstars Music 2017 in Los Angeles, cutting-edge music startup Amper presented samples of its AI-created compositions. This begs the question: Will computers eventually create quality music compositions? Whenever pioneering technologies emerge, my advice to students in digital innovation courses is not to wonder whether or not the technology will disrupt an industry, but the extent to which it will.

Five days from now Google will publish open-source tools that will focus its machine-learning engine on music and art. But one London startup, named Jukedeck, has been working on getting machines to automatically generate original music for years. You can even generate your own ditty, composed by artificial intelligence, right now on Jukedeck's website. Jukedeck lets anyone use its machine-learning engine to generate tunes hosted on its website. The engine then produces an original piece of music.

Music composition can be a challenge for many small- to medium-sized game companies, largely due to the expense and difficulty in creating original music for each level of a game. To address this, we developed a tool that automatically generates original music, by training a music generator on pieces whose style the game designer wishes to imitate. The generator then creates original music in that style in real-time, and switches between styles when signaled by the game. This software has been refined to produce music that is coherent and imitates a composer’s larger music structure.