Abstract--This project presents an AI-based system for tone replication in music production, focusing on predicting EQ parameter settings directly from audio features. Unlike traditional audio-to-audio methods, our approach generates interpretable parameter values--such as EQ band gains--that musicians can further adjust in their workflow. Using a dataset of piano recordings with systematically varied EQ settings, we evaluate both regression and neural network models. Results show that our neural network model achieves highly accurate parameter predictions, with a mean squared error of 0.0216 on multi-band tasks. The proposed system enables practical, flexible, and automated tone matching for music producers, laying the foundation for future extensions to more complex audio effects.

W e show that randomly initialized recurrent neural networks can produce arpeggios and low-frequency oscillations that are rich and configurable. In contrast to end-to-end music generation that aims to replace musicians, our approach expands their creativity while requiring no data and much less computational power . More information can be found at: https://allendia.com/ 1. INTRODUCTION Artificial intelligence continues to drive significant changes in music production. However, current methods often require vast amounts of high-quality data, which are not always readily available.

This study presents an exploratory evaluation of Music Generation Systems (MGS) within contemporary music production workflows by examining eight open-source systems. The evaluation framework combines technical insights with practical experimentation through criteria specifically designed to investigate the practical and creative affordances of the systems within the iterative, non-linear nature of music production. Employing a single-evaluator methodology as a preliminary phase, this research adopts a mixed approach utilizing qualitative methods to form hypotheses subsequently assessed through quantitative metrics. The selected systems represent architectural diversity across both symbolic and audio-based music generation approaches, spanning composition, arrangement, and sound design tasks. The investigation addresses limitations of current MGS in music production, challenges and opportunities for workflow integration, and development potential as collaborative tools while maintaining artistic authenticity. Findings reveal these systems function primarily as complementary tools enhancing rather than replacing human expertise. They exhibit limitations in maintaining thematic and structural coherence that emphasize the indispensable role of human creativity in tasks demanding emotional depth and complex decision-making. This study contributes a structured evaluation framework that considers the iterative nature of music creation. It identifies methodological refinements necessary for subsequent comprehensive evaluations and determines viable areas for AI integration as collaborative tools in creative workflows. The research provides empirically-grounded insights to guide future development in the field.

We introduce Music Source Restoration (MSR), a novel task addressing the gap between idealized source separation and real-world music production. Current Music Source Separation (MSS) approaches assume mixtures are simple sums of sources, ignoring signal degradations employed during music production like equalization, compression, and reverb. MSR models mixtures as degraded sums of individually degraded sources, with the goal of recovering original, undegraded signals. Due to the lack of data for MSR, we present RawStems, a dataset annotation of 578 songs with unprocessed source signals organized into 8 primary and 17 secondary instrument groups, totaling 354.13 hours. To the best of our knowledge, RawStems is the first dataset that contains unprocessed music stems with hierarchical categories. We consider spectral filtering, dynamic range compression, harmonic distortion, reverb and lossy codec as possible degradations, and establish U-Former as a baseline method, demonstrating the feasibility of MSR on our dataset. We release the RawStems dataset annotations, degradation simulation pipeline, training code and pre-trained models to be publicly available.

Artificial intelligence is reshaping creative domains, yet its co-creative processes, especially in group settings with novice users, remain under explored. To bridge this gap, we conducted a case study in a college-level course where nine undergraduate students were tasked with creating three original music tracks using AI tools over 10 weeks. The study spanned the entire creative journey from ideation to releasing these songs on Spotify. Participants leveraged AI for music and lyric production, cover art, and distribution. Our findings highlight how AI transforms creative workflows: accelerating ideation but compressing the traditional preparation stage, and requiring novices to navigate a challenging idea selection and validation phase. We also identified a new "collaging and refinement" stage, where participants creatively combined diverse AI-generated outputs into cohesive works. Furthermore, AI influenced group social dynamics and role division among human creators. Based on these insights, we propose the Human-AI Co-Creation Stage Model and the Human-AI Agency Model, offering new perspectives on collaborative co-creation with AI.

The rise of "bedroom producers" has democratized music creation, while challenging producers to objectively evaluate their work. To address this, we present AI TrackMate, an LLM-based music chatbot designed to provide constructive feedback on music productions. By combining LLMs' inherent musical knowledge with direct audio track analysis, AI TrackMate offers production-specific insights, distinguishing it from text-only approaches. Our framework integrates a Music Analysis Module, an LLM-Readable Music Report, and Music Production-Oriented Feedback Instruction, creating a plug-and-play, training-free system compatible with various LLMs and adaptable to future advancements. We demonstrate AI TrackMate's capabilities through an interactive web interface and present findings from a pilot study with a music producer. By bridging AI capabilities with the needs of independent producers, AI TrackMate offers on-demand analytical feedback, potentially supporting the creative process and skill development in music production. This system addresses the growing demand for objective self-assessment tools in the evolving landscape of independent music production.

In recent years, artificial intelligence (AI) has made significant progress in the field of music generation, driving innovation in music creation and applications. This paper provides a systematic review of the latest research advancements in AI music generation, covering key technologies, models, datasets, evaluation methods, and their practical applications across various fields. The main contributions of this review include: (1) presenting a comprehensive summary framework that systematically categorizes and compares different technological approaches, including symbolic generation, audio generation, and hybrid models, helping readers better understand the full spectrum of technologies in the field; (2) offering an extensive survey of current literature, covering emerging topics such as multimodal datasets and emotion expression evaluation, providing a broad reference for related research; (3) conducting a detailed analysis of the practical impact of AI music generation in various application domains, particularly in real-time interaction and interdisciplinary applications, offering new perspectives and insights; (4) summarizing the existing challenges and limitations of music quality evaluation methods and proposing potential future research directions, aiming to promote the standardization and broader adoption of evaluation techniques. Through these innovative summaries and analyses, this paper serves as a comprehensive reference tool for researchers and practitioners in AI music generation, while also outlining future directions for the field.

Effective music mixing requires technical and creative finesse, but clear communication with the client is crucial. The mixing engineer must grasp the client's expectations, and preferences, and collaborate to achieve the desired sound. The tacit agreement for the desired sound of the mix is often established using guides like reference songs and demo mixes exchanged between the artist and the engineer and sometimes verbalised using semantic terms. This paper presents the findings of a two-phased exploratory study aimed at understanding how professional mixing engineers interact with clients and use their feedback to guide the mixing process. For phase one, semi-structured interviews were conducted with five mixing engineers with the aim of gathering insights about their communication strategies, creative processes, and decision-making criteria. Based on the inferences from these interviews, an online questionnaire was designed and administered to a larger group of 22 mixing engineers during the second phase. The results of this study shed light on the importance of collaboration, empathy, and intention in the mixing process, and can inform the development of smart multi-track mixing systems that better support these practices. By highlighting the significance of these findings, this paper contributes to the growing body of research on the collaborative nature of music production and provides actionable recommendations for the design and implementation of innovative mixing tools.

Jukebox, an innovative AI system created by OpenAI, leverages the power of deep learning to generate music, complete with lyrics and vocals, in a variety of genres and styles. By training on a dataset of 1.2 million songs, Jukebox showcases an unparalleled level of sophistication in music generation, pushing the boundaries of what AI can achieve in the creative arts. At the core of Jukebox lies a cutting-edge neural network architecture, known as a Variational Autoencoder (VAE). The VAE's role is to encode and decode the complex musical information found within the training dataset. This encoding-decoding process enables Jukebox to generate novel and diverse musical compositions by sampling from the latent space, a mathematical representation of the underlying structure of the dataset.

Artificial intelligence (AI) is playing an increasingly important role in the music production industry and is likely to continue to do so in the coming years. Here are a few ways that AI is already being used in music production, and how it could evolve in the next five years.