triz-audio-timeline

name	triz-audio-timeline
description	TRIZ inventive principles applied to audio timeline segmentation problems

When to use

Use when facing fundamental tradeoffs in audio segmentation: speed vs accuracy, sensitivity vs specificity, granularity vs simplicity. TRIZ provides structured problem-solving for these contradictions.

TRIZ core concepts

Key principles for audio segmentation

1. Segmentation: Divide system into independent parts
2. Extraction: Remove unwanted portions (noise, silence)
3. Local quality: Different properties in different parts
4. Asymmetry: Use asymmetric solutions for asymmetric problems
5. Prior action: Prepare favorable conditions in advance

Contradictions in audio VAD

Technical contradiction: Speed vs Accuracy

Parameter	Improving Speed	Hurting Accuracy
Larger frame size (50ms)	Faster processing	Misses short speech
Shorter window	Better granularity	More noise sensitivity

TRIZ solution: Segmentation principle

• Divide audio into chunks, process in parallel
• Apply coarse detection first, refine on detected speech regions only

Physical contradiction: Sensitivity vs Specificity

Requirement	Opposite Requirement
Detect quiet speech (whispers)	Reject ambient noise
Capture short utterances	Ignore mic artifacts

TRIZ solution: Separation principles

• Separation in space: Use frequency bandpass (300-3400Hz) to isolate speech
• Separation in time: Apply hysteresis (hangover) to bridge short gaps
• Separation in condition: Different thresholds for different profiles

Ideal Final Result (IFR) for non-voice detection

IFR statement

"The non-voice detector should identify all non-speech regions without any false positives or false negatives, without consuming resources for speech regions, and without requiring manual tuning."

Practical IFR decomposition

1. Primary function: Detect non-voice with >95% accuracy
2. Ideal behavior: No manual threshold adjustment needed
3. Resource constraint: CPU-only, <1x realtime
4. Boundary: Works across all genres without retraining

IFR gap analysis

Gap	Current State	IFR
Accuracy	~85-90%	95%+
Tuning	Manual per-profile	Self-calibrating
Genres	4 profiles	Universal

Resource tradeoff analysis

Available resources

• Time: Processing speed, latency
• Substance: Memory usage, storage for corrections
• Information: Correction feedback data
• Precision: Temporal resolution of boundaries

Resource-cost diagram

                    High Accuracy
                         ↑
                         │     ┌─────────────────┐
                         │     │  Ideal region   │
    Cost                 │     │  (unreachable)  │
                         │     └─────────────────┘
                         │
                         └─────────────────────────→ Low Cost

Optimization strategy

1. Accept: ~85% accuracy is sufficient for most applications
2. Prioritize: Precision (avoid false non-voice) over recall for drama
3. Leverage: Self-learning calibration to improve over time

Separation principles in timeline construction

Time-based separation

Speech segments: [====]     [========]    [===]
                    ↑ gap        ↑ gap
                  merge if     merge if
                 gap < 200ms   gap < 200ms

Non-voice segments: [──────]  [────────]  [──]
                   gap ≥ 1s   gap ≥ 1s    skip

Condition-based separation (by profile)

action:   threshold +0.010 → lower sensitivity, higher noise immunity
drama:    threshold -0.001 → higher sensitivity, captures whispers

Hierarchical separation

1. Coarse pass: 50ms frames, rough boundaries
2. Fine pass: 20ms frames, refine boundaries
3. Smooth pass: Apply hangover, remove flicker
4. Merge pass: Close gaps, enforce min durations

Problem-solving patterns

Pattern 1: Detecting speech in noisy backgrounds

Problem: Explosions mask dialogue in action scenes Contradiction: Need low threshold (sensitivity) vs high threshold (noise rejection) TRIZ solution: Dynamorphism + feedback

// Adaptive threshold based on noise floor
let noise_floor = median(background_frames.rms);
let adaptive_threshold = noise_floor * 4.0 + profile_delta;

Pattern 2: Handling variable speaking rates

Problem: Fast dialogue creates short segments; slow dialogue creates long pauses Contradiction: Small min_speech_ms (capture quick exchanges) vs large (reject noise) TRIZ solution: Segmentation + local quality

// Variable thresholds by region type
let threshold = match detect_region_type(segment) {
    RegionType::Dialogue => base_threshold - 0.002,
    RegionType::Narration => base_threshold,
    RegionType::Action => base_threshold + 0.005,
};

Pattern 3: False non-voice at boundaries

Problem: Leading/trailing silence incorrectly included as non-voice Contradiction: Want complete coverage vs want only meaningful segments TRIZ solution: Prior action + extraction

// Trim leading/trailing segments shorter than threshold
const MIN_EDGE_SEGMENT_MS: u64 = 500;  // Discard edge gaps < 500ms

Application checklist

When solving VAD problems with TRIZ:

• Identify the technical contradiction (what improves, what degrades)
• Identify the physical contradiction (two opposite requirements)
• Apply separation principles if contradiction is irreconcilable
• Check IFR: what would the ideal system do?
• Use available resources efficiently (avoid over-engineering)
• Consider dynamic solutions (adaptive threshold, multi-pass)

Related skills

• audio-vad-cpu: Energy-based VAD implementation
• nonvoice-segmentation: Segment post-processing
• self-learning-calibration: Feedback-driven improvement