Multimodal Audio Generation

Multimodal generation connects audio with other modalities — video, images, text, motion — to create richer, more contextual AI music and sound experiences.

Why Multimodal?

Music rarely exists in isolation. It accompanies:

• Video: film scores, YouTube content, advertisements
• Games: adaptive soundtracks, sound effects
• Images: album art → music mood, visual → sound
• Dance/Motion: movement-synchronized music
• Text: lyrics, descriptions, narratives

Multimodal models that understand these connections can generate more contextually appropriate audio.

Video-to-Audio

The Task

Given a video (or video segment), generate a matching soundtrack or sound effects.

$$a = G_\theta(v, c_{\text{text}})$$

where $v$ is the video and $c_{\text{text}}$ is an optional text prompt.

Approaches

1. Video Feature Extraction + Audio Generation

Video ──▶ Video Encoder (CLIP/ViT) ──▶ Temporal Features
                                            │
                                       Cross-attention
                                            │
Text ──▶ Text Encoder ──▶ ──────────────────┘
                                            │
                                            ▼
                                    Audio Diffusion Model ──▶ Audio

2. Joint Embedding Space

Models like ImageBind (Meta) create a shared embedding space across 6 modalities:

• Text, image, audio, video, depth, thermal

$$\text{sim}(\mathbf{e}_{\text{video}}, \mathbf{e}_{\text{audio}}) \begin{cases} \text{high} & \text{if semantically related} \\ \text{low} & \text{otherwise} \end{cases}$$

3. Autoregressive with Interleaved Tokens

Model audio and video tokens in a single sequence, allowing the model to generate audio tokens conditioned on video tokens.

Challenges

Challenge	Detail
Temporal alignment	Music beats must sync with visual events
Mood matching	Audio mood must match visual tone
Length flexibility	Videos vary in length
Sound effect vs. music	Different content types needed for different scenes

Key Models

Model	Approach	Input	Output
IM2WAV	CLIP features + diffusion	Image/Video	Audio
Seeing and Hearing	Latent diffusion	Video	Audio
V2A (Google DeepMind)	Diffusion, video-conditioned	Video + text	Audio
Movie Gen Audio (Meta)	Transformer + flow matching	Video + text	Audio

Image-to-Music

The Task

Generate music that "sounds like" an image looks.

$$a = G_\theta(\text{image})$$

How It Works

1. Extract visual features: color palette, scene type, mood, objects
2. Map to audio semantics: bright colors → bright timbres, dark scenes → dark harmony
3. Generate: condition audio model on visual embeddings

Practical Applications

• Generate album cover-matching music
• Create soundscapes for visual art
• Accessibility: "hear" images

Text + Audio (Instruction-Following)

Beyond Simple Prompts

Advanced models follow complex text instructions:

• "Make the drums louder in the chorus"
• "Add a guitar solo after the second verse"
• "Change the style from jazz to bossa nova"

This requires models that understand:

1. The current audio state
2. The instruction semantics
3. How to modify audio to satisfy the instruction

Audio Editing via Text

$$a_{\text{edited}} = G_\theta(a_{\text{input}}, t_{\text{instruction}})$$

Models like AUDIT and InstructME perform text-guided audio editing:

• Add or remove instruments
• Change style or mood
• Modify specific sections
• Adjust mix parameters

Motion-to-Music

Dance-Music Synchronization

Generate music that matches dance movements:

$$a = G_\theta(\text{pose\_sequence})$$

Input: sequence of body poses (from motion capture or video) Output: music with beats aligned to movement accents

Applications

• Dance video soundtracks
• Interactive dance games
• Choreography assistance

Multimodal Understanding Models

AudioLM + Visual

Combine audio language models with visual understanding:

• Process audio and visual tokens in a unified transformer
• Generate either modality conditioned on the other
• Cross-modal attention enables rich interactions

Unified Multimodal Models

Large models that process multiple modalities simultaneously:

Model	Modalities	Architecture
ImageBind	6 modalities	Joint embedding
CoDi	Any-to-any	Composable diffusion
NExT-GPT	Any-to-any	LLM + modality encoders/decoders
Gemini	Text, image, audio, video	Multimodal transformer

Technical Challenges

Temporal Alignment

Video and audio have different temporal granularities:

• Video: 24–60 fps
• Audio: 44,100 samples/s
• Music events: beats, ~2 per second

Alignment requires:

• Shared temporal representations
• Cross-modal attention at appropriate resolutions
• Beat detection and synchronization

Semantic Gap

The relationship between visual and auditory semantics is often subjective:

• What music "goes with" a sunset? (Depends on context and culture)
• Should scary visuals always have scary music? (Not necessarily)

Models must learn flexible, context-dependent associations rather than rigid mappings.

Evaluation

Multimodal evaluation is harder than unimodal:

Metric	What It Measures
Audio quality (FAD)	Sound quality independent of visual match
Semantic alignment	Does audio match video content?
Temporal alignment	Are audio events synchronized with visual events?
Human preference	Overall subjective quality and fit

Most evaluation relies heavily on human judgments, as no single objective metric captures multimodal quality.

Future Directions

• Interactive multimodal generation: real-time adjustment of music based on visual input
• Scene-aware adaptive music: film score generation that responds to scene changes
• Spatial audio for VR/AR: 3D sound generation matched to visual environments
• Cross-cultural multimodal learning: understanding culture-specific audio-visual associations