sound-engineer

Sound Engineer: Spatial Audio, Procedural Sound & App UX Audio

Expert audio engineer for interactive media: games, VR/AR, and mobile apps. Specializes in spatial audio, procedural sound generation, middleware integration, and UX sound design.

When to Use This Skill

✅ Use for:

• Spatial audio (HRTF, binaural, Ambisonics)
• Procedural sound (footsteps, wind, environmental)
• Game audio middleware (Wwise, FMOD)
• Adaptive/interactive music systems
• UI/UX sound design (clicks, notifications, feedback)
• Sonic branding (audio logos, brand sounds)
• iOS/Android audio session handling
• Haptic-audio coordination
• Real-time DSP (reverb, EQ, compression)

❌ Do NOT use for:

• Music composition/production → DAW tools (Logic, Ableton)
• Voice synthesis/cloning → voice-audio-engineer
• Film audio post-production → linear editing workflows
• Podcast editing → standard audio editors
• Hardware microphone setup → specialized domain

MCP Integrations

MCP	Purpose
ElevenLabs	text_to_sound_effects - Generate UI sounds, notifications, impacts
Firecrawl	Research Wwise/FMOD docs, DSP algorithms, platform guidelines
WebFetch	Fetch Apple/Android audio session documentation

Expert vs Novice Shibboleths

Topic	Novice	Expert
Spatial audio	"Just pan left/right"	Uses HRTF convolution for true 3D; knows Ambisonics for VR head tracking
Footsteps	"Use 10-20 samples"	Procedural synthesis: infinite variation, tiny memory, parameter-driven
Middleware	"Just play sounds"	Uses RTPC for continuous params, Switches for materials, States for music
Adaptive music	"Crossfade tracks"	Horizontal re-orchestration (layers) + vertical remixing (stems)
UI sounds	"Any click sound works"	Designs for brand consistency, accessibility, haptic coordination
iOS audio	"AVAudioPlayer works"	Knows AVAudioSession categories, interruption handling, route changes
Distance rolloff	Linear attenuation	Inverse square with reference distance; logarithmic for realism
CPU budget	"Audio is cheap"	Knows 5-10% budget; HRTF convolution is expensive (2ms/source)

Common Anti-Patterns

Anti-Pattern: Sample-Based Footsteps at Scale

What it looks like: 20 footstep samples × 6 surfaces × 3 intensities = 360 files (180MB) Why it's wrong: Memory bloat, repetition audible after 20 minutes of play What to do instead: Procedural synthesis - impact + texture layers, infinite variation from parameters When samples OK: Small games, very specific character sounds

Anti-Pattern: HRTF for Every Sound

What it looks like: Full HRTF convolution on 50 simultaneous sources Why it's wrong: 50 × 2ms = 100ms CPU time; destroys frame budget What to do instead: HRTF for 3-5 important sources; Ambisonics for ambient bed; simple panning for distant/unimportant

Anti-Pattern: Ignoring Audio Sessions (Mobile)

What it looks like: App audio stops when user gets a phone call, never resumes Why it's wrong: iOS/Android require explicit session management What to do instead: Implement AVAudioSession (iOS) or AudioFocus (Android); handle interruptions, route changes

Anti-Pattern: Hard-Coded Sounds

What it looks like: PlaySound("footstep_concrete_01.wav") Why it's wrong: No variation, no parameter control, can't adapt to context What to do instead: Use middleware events with Switches/RTPCs; procedural generation for environmental sounds

Anti-Pattern: Loud UI Sounds

What it looks like: Every button click at -3dB, same volume as gameplay audio Why it's wrong: UI sounds should be subtle, never fatiguing; violates platform guidelines What to do instead: UI sounds at -18 to -24dB; use short, high-frequency transients; respect system volume

Evolution Timeline

Pre-2010: Fixed Audio

• Sample playback only
• Basic stereo panning
• Limited real-time processing

2010-2015: Middleware Era

• Wwise/FMOD become standard
• RTPC and State systems mature
• Basic HRTF support

2016-2020: VR Audio Revolution

• Ambisonics for VR head tracking
• Spatial audio APIs (Resonance, Steam Audio)
• Procedural audio gains traction

2021-2024: AI & Mobile

• ElevenLabs/AI sound effect generation
• Apple Spatial Audio for AirPods
• Procedural audio standard for AAA
• Haptic-audio design becomes discipline

2025+: Current Best Practices

• AI-assisted sound design
• Neural audio codecs
• Real-time voice transformation
• Personalized HRTF from photos

Core Concepts

Spatial Audio Approaches

Approach	CPU Cost	Quality	Use Case
Stereo panning	~0.01ms	Basic	Distant sounds, many sources
HRTF convolution	~2ms/source	Excellent	Close/important 3D sounds
Ambisonics	~1ms total	Good	VR, many sources, head tracking
Binaural (simple)	~0.1ms/source	Decent	Budget/mobile spatial

HRTF: Convolves audio with measured ear impulse responses (512-1024 taps). Creates convincing 3D positioning including elevation.

Ambisonics: Encodes sound field as spherical harmonics (W,X,Y,Z for 1st order). Rotation-invariant, efficient for many sources.

// Key insight: encode once, rotate cheaply
AmbisonicSignal encode(mono_input, direction) {
    return {
        mono * 0.707f,      // W (omnidirectional)
        mono * direction.x, // X (front-back)
        mono * direction.y, // Y (left-right)
        mono * direction.z  // Z (up-down)
    };
}

Procedural Footsteps

Why procedural beats samples:

• ✅ Infinite variation (no repetition)
• ✅ Tiny memory (~50KB vs 5-10MB)
• ✅ Parameter-driven (speed → impact force)
• ✅ Surface-aware from physics materials

Core synthesis:

1. Impact burst (20ms noise + resonant tone)
2. Surface texture (gravel = granular, grass = filtered noise)
3. Debris (scattered micro-impacts)
4. Surface EQ (metal = bright, grass = muffled)

// Surface resonance frequencies (expert knowledge)
float get_resonance(Surface s) {
    switch(s) {
        case Concrete: return 150.0f;  // Low, dull
        case Wood:     return 250.0f;  // Mid, warm
        case Metal:    return 500.0f;  // High, ringing
        case Gravel:   return 300.0f;  // Crunchy mid
        default:       return 200.0f;
    }
}

Wwise/FMOD Integration

Key abstractions:

• Events: Trigger sounds (footstep, explosion, ambient loop)
• RTPC: Continuous parameters (speed 0-100, health 0-1)
• Switches: Discrete choices (surface type, weapon type)
• States: Global context (music intensity, underwater)

// Material-aware footsteps via Wwise
void OnFootDown(FHitResult& hit) {
    FString surface = DetectSurface(hit.PhysMaterial);
    float speed = GetVelocity().Size();

    SetSwitch("Surface", surface, this);        // Concrete/Wood/Metal
    SetRTPCValue("Impact_Force", speed/600.0f); // 0-1 normalized
    PostEvent(FootstepEvent, this);
}

UI/UX Sound Design

Principles for app sounds:

1. Subtle - UI sounds at -18 to -24dB
2. Short - 50-200ms for most interactions
3. Consistent - Same family/timbre across app
4. Accessible - Don't rely solely on audio for feedback
5. Haptic-paired - iOS haptics should match audio characteristics

Sound types:

Category	Examples	Duration	Character
Tap feedback	Button, toggle	30-80ms	Soft, high-frequency click
Success	Save, send, complete	150-300ms	Rising, positive tone
Error	Invalid, failed	200-400ms	Descending, minor tone
Notification	Alert, reminder	300-800ms	Distinctive, attention-getting
Transition	Screen change, modal	100-250ms	Whoosh, subtle movement

iOS/Android Audio Sessions

iOS AVAudioSession categories:

• .ambient - Mixes with other audio, silenced by ringer
• .playback - Interrupts other audio, ignores ringer
• .playAndRecord - For voice apps
• .soloAmbient - Default, silences other audio

Critical handlers:

• Interruption (phone call)
• Route change (headphones unplugged)
• Secondary audio (Siri)

// Proper iOS audio session setup
func configureAudioSession() {
    let session = AVAudioSession.sharedInstance()
    try? session.setCategory(.playback, mode: .default, options: [.mixWithOthers])
    try? session.setActive(true)

    NotificationCenter.default.addObserver(
        self,
        selector: #selector(handleInterruption),
        name: AVAudioSession.interruptionNotification,
        object: nil
    )
}

Performance Targets

Operation	CPU Time	Notes
HRTF convolution (512-tap)	~2ms/source	Use FFT overlap-add
Ambisonic encode	~0.1ms/source	Very efficient
Ambisonic decode (binaural)	~1ms total	Supports many sources
Procedural footstep	~1-2ms	vs 500KB per sample
Wind synthesis	~0.5ms/frame	Real-time streaming
Wwise event post	<0.1ms	Negligible
iOS audio callback	5-10ms budget	At 48kHz/512 samples

Budget guideline: Audio should use 5-10% of frame time.

Quick Reference

Spatial Audio Decision Tree

• VR with head tracking? → Ambisonics
• Few important sources? → Full HRTF
• Many background sources? → Simple panning + distance rolloff
• Mobile with limited CPU? → Binaural (simple) or panning

When to Use Procedural Audio

• Environmental (wind, rain, fire) → Always procedural
• Footsteps → Procedural for large games, samples for small
• UI sounds → Generated once, then cached
• Impacts/explosions → Hybrid (procedural + sample layers)

Platform Audio Sessions

• Game with music: .ambient + mixWithOthers
• Meditation/focus app: .playback (interrupt music)
• Voice chat: .playAndRecord
• Video player: .playback

---

Content truncated.