AI Harmonizer: Expanding Vocal Expression with a Generative Neurosymbolic Music AI System
- URL: http://arxiv.org/abs/2506.18143v1
- Date: Sun, 22 Jun 2025 19:13:31 GMT
- Title: AI Harmonizer: Expanding Vocal Expression with a Generative Neurosymbolic Music AI System
- Authors: Lancelot Blanchard, Cameron Holt, Joseph A. Paradiso,
- Abstract summary: The AI Harmonizer autonomously generates musically coherent four-part harmonies without requiring prior harmonic input from the user.<n>We present our methods, explore potential applications in performance and composition, and discuss future directions for real-time implementations.
- Score: 3.356609500886644
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Vocals harmonizers are powerful tools to help solo vocalists enrich their melodies with harmonically supportive voices. These tools exist in various forms, from commercially available pedals and software to custom-built systems, each employing different methods to generate harmonies. Traditional harmonizers often require users to manually specify a key or tonal center, while others allow pitch selection via an external keyboard-both approaches demanding some degree of musical expertise. The AI Harmonizer introduces a novel approach by autonomously generating musically coherent four-part harmonies without requiring prior harmonic input from the user. By integrating state-of-the-art generative AI techniques for pitch detection and voice modeling with custom-trained symbolic music models, our system arranges any vocal melody into rich choral textures. In this paper, we present our methods, explore potential applications in performance and composition, and discuss future directions for real-time implementations. While our system currently operates offline, we believe it represents a significant step toward AI-assisted vocal performance and expressive musical augmentation. We release our implementation on GitHub.
Related papers
- LeVo: High-Quality Song Generation with Multi-Preference Alignment [49.94713419553945]
We introduce LeVo, an LM-based framework consisting of LeLM and a music accompaniment.<n>LeVo is capable of parallelly modeling two types of tokens: mixed tokens, which represent the combined audio of vocals and to achieve vocal-instrument harmony, and dual-track tokens, which separately encode vocals and accompaniment.<n> Experimental results demonstrate that LeVo consistently outperforms existing methods on both objective and subjective metrics.
arXiv Detail & Related papers (2025-06-09T07:57:24Z) - Machine Learning Approaches to Vocal Register Classification in Contemporary Male Pop Music [49.1574468325115]
In pop music, where a single artist may use a variety of timbre's and textures to achieve a desired quality, it can be difficult to identify what vocal register within the vocal range a singer is using.<n>This paper presents two methods for classifying vocal registers in an audio signal of male pop music through the analysis of textural features of mel-spectrogram images.
arXiv Detail & Related papers (2025-05-16T15:41:28Z) - SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation [75.86473375730392]
SongGen is a fully open-source, single-stage auto-regressive transformer for controllable song generation.<n>It supports two output modes: mixed mode, which generates a mixture of vocals and accompaniment directly, and dual-track mode, which synthesizes them separately.<n>To foster community engagement and future research, we will release our model weights, training code, annotated data, and preprocessing pipeline.
arXiv Detail & Related papers (2025-02-18T18:52:21Z) - Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt [50.25271407721519]
We propose Prompt-Singer, the first SVS method that enables attribute controlling on singer gender, vocal range and volume with natural language.<n>We adopt a model architecture based on a decoder-only transformer with a multi-scale hierarchy, and design a range-melody decoupled pitch representation.<n>Experiments show that our model achieves favorable controlling ability and audio quality.
arXiv Detail & Related papers (2024-03-18T13:39:05Z) - Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing [10.159860910939686]
Loop Copilot is a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface.
The system uses a large language model to interpret user intentions and select appropriate AI models for task execution.
arXiv Detail & Related papers (2023-10-19T01:20:12Z) - Performance Conditioning for Diffusion-Based Multi-Instrument Music
Synthesis [15.670399197114012]
We propose enhancing control of multi-instrument synthesis by conditioning a generative model on a specific performance and recording environment.
Performance conditioning is a tool indicating the generative model to synthesize music with style and timbre of specific instruments taken from specific performances.
Our prototype is evaluated using uncurated performances with diverse instrumentation and state-of-the-art FAD realism scores.
arXiv Detail & Related papers (2023-09-21T17:44:57Z) - Multi-instrument Music Synthesis with Spectrogram Diffusion [19.81982315173444]
We focus on a middle ground of neural synthesizers that can generate audio from MIDI sequences with arbitrary combinations of instruments in realtime.
We use a simple two-stage process: MIDI to spectrograms with an encoder-decoder Transformer, then spectrograms to audio with a generative adversarial network (GAN) spectrogram inverter.
We find this to be a promising first step towards interactive and expressive neural synthesis for arbitrary combinations of instruments and notes.
arXiv Detail & Related papers (2022-06-11T03:26:15Z) - Unsupervised Cross-Domain Singing Voice Conversion [105.1021715879586]
We present a wav-to-wav generative model for the task of singing voice conversion from any identity.
Our method utilizes both an acoustic model, trained for the task of automatic speech recognition, together with melody extracted features to drive a waveform-based generator.
arXiv Detail & Related papers (2020-08-06T18:29:11Z) - RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement
Learning [69.20460466735852]
This paper presents a deep reinforcement learning algorithm for online accompaniment generation.
The proposed algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part.
arXiv Detail & Related papers (2020-02-08T03:53:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.