MAGE: Modality-Agnostic Music Generation and Target-Source Extraction
Authors: Muhammad Usama Saleem, Tejasvi Ravi, Tianyu Xu, Rajeev Nongpiur, Ishan Chatterjee, Mayur Jagdishbhai Patel, Pu Wang
Organizations: Google · University of North Carolina at Charlotte
Abstract
Recent advances in multimodal audio generation have enabled music synthesis from text, visual cues, and other high-level conditions. However, most systems are designed for a single operating mode: either generating music without a reference mixture or extracting a target source from an existing mixture. This fixed-task design limits their use when different combinations of text, visual, and mixture inputs are available. To address this gap, we propose MAGE, a modality-agnostic framework for conditional music generation and mixture-grounded target-source extraction within a shared continuous latent space. Our approach introduces three key components. First, a Controlled Multimodal FluxFormer models the conditional flow from noise to a target audio latent, enabling the same backbone to operate with or without a mixture condition. Second, Audio-Visual Nexus Alignment maps frame-level visual features onto the audio latent sequence, allowing visual evidence to condition the generation process at the audio-token level. Third, a cross-gated modulation mechanism uses the aligned visual representation to regulate intermediate audio features, while text provides separate semantic guidance. We further train MAGE with dynamic modality masking, exposing the same model to text-only, visual-only, joint text-visual, mixture-conditioned, and unconditional configurations. Experiments on the MUSIC benchmark evaluate MAGE under separate protocols for mixture-free generation and mixture-grounded target-source extraction. The results show that MAGE provides a shared conditioning interface across both settings, and that the proposed alignment and gating components improve interference suppression in the extraction task.
Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a unified multimodal modeling framework, 2) large-scale, high-quality training data, and 3) the prohibitive inference cost of multi-step diffusion sampling. As such, we propose AudioX-Turbo, a unified and efficient framework for anything-to-audio generation that integrates varied multimodal conditions (i.e., text, video, and audio signals) in this work. AudioX-Turbo follows a teacher-student paradigm. The teacher AudioX-Base is built on a Multimodal Diffusion Transformer with a Multimodal Adaptive Fusion module that aligns diverse multimodal inputs for high-fidelity synthesis, and is then distilled into the few-step student AudioX-Turbo via Distribution Matching Distillation adapted to flow matching, complemented by a diffusion-based discriminator for high-quality few-step generation. To support the training of AudioX-Turbo, we construct a large-scale, high-quality dataset, IF-caps-Pro, comprising approximately 9.2M samples curated through a two-stage data collection and annotation pipeline. We benchmark AudioX-Turbo across a wide range of tasks, finding that our model achieves superior performance, especially on text-to-audio and text-to-music generation, while operating at only 4 sampling steps and requiring approximately 25x fewer function evaluations (NFE) than multi-step baselines. These results demonstrate that our method is capable of audio generation under flexible multimodal control, showing efficient and powerful instruction-following capabilities. The code and datasets will be available at https://zeyuet.github.io/AudioX-Turbo/.
Recent audio generation systems have progressed from single-modality synthesis to generating complex acoustic scenes containing speech, music, and sound effects. Therefore, evaluating these models requires assessing multiple interacting capabilities, including semantic fidelity, speaker consistency, and temporal control, yet existing benchmarks focus on isolated domains or coarse-grained descriptions. To address this gap, we introduce the Multi-control Mixed Audio Generation (MMAG) benchmark. MMAG contains approximately 4,000 manually verified audio clips with rich annotations covering speech content, speaker identity, music attributes, sound events, and temporal relationships, together with dedicated subsets for voice cloning and timestamp-conditioned generation. We further propose a systematic evaluation protocol that measures acoustic fidelity, speech quality, semantic alignment, and temporal accuracy. Benchmarking representative agentic orchestrators, unified audio-visual generation models, and native mixed-audio generators reveals substantial performance trade-offs across these capabilities, with no existing model performing consistently well. Our results highlight the remaining challenges of controllable mixed audio generation and establish MMAG as a comprehensive benchmark for future research.
Unifying speech, sound, and music generation in one model is hindered by tradeoffs between fidelity, end-to-end training, in-context conditioning, and variable-length synthesis that no current paradigm fully resolves. To address this challenge, we present AudioCALM, a universal audio generation framework that extends autoregressive (AR) next-token prediction from discrete tokens to continuous audio latents: a thin flow-matching head replaces the softmax to predict rectified-flow velocities at each position, and a block-causal AR-Flow attention pattern produces arbitrary-length output. Joint training of multiple audio generation tasks faces an asymmetric text--audio mismatch: speech transcripts align to specific time spans and demand tight, time-aligned attention, whereas sound and music captions describe only overall semantics and rely on diffuse, holistic attention; mixing the two disproportionately degrades sound and music generation. We address this asymmetry at two levels: a data reformulation strategy that unifies all three tasks under a single description-style conditioning interface, and a novel architecture Asymmetric Mixture-of-Modality-Experts (A-MoME), which adds a dedicated residual expert for speech while sound and music share the backbone, incurring no inference overhead on non-speech inputs. Experimental results demonstrate that AudioCALM matches modality-specific state-of-the-art and outperforms prior unified baselines on speech, sound, and music generation benchmarks.