1345  Audio Feature Extraction for IoT

1345.1 Learning Objectives

By the end of this chapter, you will be able to:

  • Understand MFCC Extraction: Implement the MFCC pipeline for speech and audio recognition
  • Design Keyword Spotting Systems: Build wake word detection for voice assistants
  • Optimize Audio Processing: Deploy efficient audio ML on edge devices
  • Apply Hybrid Architectures: Design edge-cloud voice recognition systems

1345.2 Prerequisites

NoteChapter Series: Modeling and Inferencing

This is part 5 of the IoT Machine Learning series:

  1. ML Fundamentals - Core concepts
  2. Mobile Sensing - HAR, transportation
  3. IoT ML Pipeline - 7-step pipeline
  4. Edge ML & Deployment - TinyML
  5. Audio Feature Processing (this chapter) - MFCC, keyword recognition
  6. Feature Engineering - Feature design
  7. Production ML - Monitoring

1345.3 MFCC: The Gold Standard for Voice Recognition

Edge devices like Amazon Alexa and Google Home use MFCC (Mel-Frequency Cepstral Coefficients) to recognize wake words locally before streaming to the cloud.

1345.3.1 Why MFCC for IoT Voice Commands?

Raw audio is extremely high-dimensional and computationally expensive:

Metric Raw Audio MFCC
Sampling 16 kHz = 16,000 samples/sec 100 frames/sec
Per frame 400 samples (25ms) 12-13 coefficients
Storage 32 KB/second 4.8 KB/second
Reduction Baseline 6.7x smaller

Real-World Impact: - Alexa wake word detection: Runs on Cortex-M4 using <10KB RAM - Google Assistant “Hey Google”: 14KB model, <100ms latency - Privacy benefit: Only MFCCs computed locally; raw audio stays on device

1345.3.2 The MFCC Extraction Pipeline

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flowchart LR
    Audio[Audio Signal<br/>16 kHz sampling] --> PreEmph[Pre-emphasis<br/>Boost high freq]
    PreEmph --> Window[Windowing<br/>25ms frames<br/>10ms overlap]
    Window --> FFT[FFT<br/>Time to Frequency]
    FFT --> Mel[Mel Filterbank<br/>26-40 filters<br/>Human ear scale]
    Mel --> Log[Log Compression<br/>Loudness perception]
    Log --> DCT[DCT Transform<br/>Decorrelate features]
    DCT --> MFCC[MFCCs<br/>12-13 coefficients]

    style Audio fill:#E67E22,stroke:#2C3E50,color:#fff
    style PreEmph fill:#ecf0f1,stroke:#2C3E50,color:#333
    style Window fill:#ecf0f1,stroke:#2C3E50,color:#333
    style FFT fill:#ecf0f1,stroke:#2C3E50,color:#333
    style Mel fill:#ecf0f1,stroke:#2C3E50,color:#333
    style Log fill:#ecf0f1,stroke:#2C3E50,color:#333
    style DCT fill:#ecf0f1,stroke:#2C3E50,color:#333
    style MFCC fill:#16A085,stroke:#2C3E50,color:#fff

Figure 1345.1: Audio to MFCC Feature Extraction Pipeline

1345.3.3 Step-by-Step MFCC Calculation

1. Pre-emphasis Filter

Boosts high-frequency components that are attenuated in human speech:

y[n] = x[n] - 0.97 × x[n-1]
  • Compensates for vocal tract dampening
  • Improves SNR for fricatives (s, sh, f sounds)

2. Windowing (Frame Blocking)

Splits continuous audio into short frames:

Parameter Value Rationale
Frame size 25ms (400 samples at 16 kHz) Short enough for stationarity
Frame shift 10ms (160 samples) 60% overlap for smooth transitions
Window function Hamming Reduces spectral leakage

Why overlap? Speech characteristics change gradually. Overlapping ensures smooth transitions aren’t missed.

3. Fast Fourier Transform (FFT)

Converts time-domain signal to frequency spectrum:

Input Output
400 time-domain samples 256 frequency bins (0-8 kHz)

4. Mel Filterbank

Applies 26-40 triangular filters spaced according to the Mel scale, mimicking human auditory perception:

Frequency Range Mel Scale Filter Spacing
0-1000 Hz Linear Evenly spaced
1000-8000 Hz Logarithmic Increasingly wider

The Mel scale formula:

Mel(f) = 2595 × log₁₀(1 + f/700)

Why Mel scale? Humans distinguish low-frequency differences (100 Hz vs 200 Hz) better than high-frequency (5000 Hz vs 5100 Hz). Mel filters allocate more filters to perceptually-important low frequencies.

5. Log Compression

S[m] = log(Energy[m])
  • Mimics human loudness perception (logarithmic)
  • Compresses dynamic range
  • Robust to volume variations

6. Discrete Cosine Transform (DCT)

Decorrelates the log filterbank energies:

Input Output
40 log filterbank energies 12-13 MFCCs

Why only 12-13 coefficients? - First 12-13 capture phoneme information (speech content) - Higher coefficients (14+) capture speaker-specific characteristics - For keyword detection, we want speaker-independent features

Diagram showing MFCC extraction pipeline with 7 stages: raw audio waveform at 16kHz, pre-emphasized signal, windowed frames, FFT spectrum, Mel filterbank, log compression, and DCT producing 12-13 MFCC coefficients.
Figure 1345.2: MFCC feature extraction from audio signal

1345.4 Edge vs Cloud Voice Recognition

Approach Latency Privacy Power Use Case
Edge MFCC + Wake Word <100ms High (audio stays local) Low Always-listening devices
Cloud Full ASR 200-500ms Low (sends audio) Medium Complex commands
Hybrid 150ms Medium Low Smart speakers (Alexa)

Why hybrid is optimal: - Edge wake word detection uses <1% CPU on MCU - Only activates cloud streaming after wake word confirmed - Preserves privacy (99% of audio never leaves device) - Cloud handles complex natural language understanding

1345.5 Worked Example: Wake Word Detection

System Architecture:

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flowchart TB
    subgraph EdgeDevice["Edge Device (Cortex-M4)"]
        Mic[Microphone<br/>16 kHz sampling] --> Buffer[Circular Buffer<br/>25ms frames]
        Buffer --> MFCC1[MFCC Extraction<br/>12 coefficients/frame]
        MFCC1 --> NN[Tiny Neural Network<br/>8 KB model<br/>3 layers]
        NN --> Threshold{Confidence<br/>> 90%?}
    end

    subgraph Cloud["Cloud (AWS/Google)"]
        ASR[Full ASR<br/>Complex NLU]
        Response[Generate Response]
    end

    Threshold -->|No| Buffer
    Threshold -->|Yes - Wake Word!| Stream[Stream Audio<br/>to Cloud]
    Stream --> ASR
    ASR --> Response
    Response --> Speaker[Play Response]

    style Mic fill:#E67E22,stroke:#2C3E50,color:#fff
    style MFCC1 fill:#16A085,stroke:#2C3E50,color:#fff
    style NN fill:#2C3E50,stroke:#16A085,color:#fff
    style Threshold fill:#E67E22,stroke:#2C3E50,color:#fff
    style ASR fill:#7F8C8D,stroke:#2C3E50,color:#fff

Figure 1345.3: Voice Assistant Wake Word Detection with Edge MFCC

1345.5.1 Step-by-step Execution

  1. Continuous MFCC computation (always running):
    • Device captures 25ms audio frames (10ms shift)
    • Computes 12 MFCCs per frame → 100 frames/second
    • Power consumption: ~5 mW (negligible)
  2. Wake word detection (small neural network):
    • Input: Last 1 second of MFCCs (100 frames × 12 coefficients = 1200 features)
    • Model: 3-layer fully connected network (8 KB total)
    • Output: Probability “Hey Alexa” was spoken
    • Inference time: <50ms on Cortex-M4
  3. Threshold check:
    • If confidence > 90% → Wake word detected!
    • If confidence < 90% → Continue monitoring, discard audio
  4. Cloud activation (only after wake word):
    • Start streaming raw audio to cloud
    • Cloud performs full ASR + NLU
    • Generate and stream response
  5. Return to low-power mode:
    • After command completes, stop cloud streaming
    • Return to MFCC-only wake word monitoring
    • Battery life: Months to years (vs. hours if always streaming)

1345.5.2 Performance Metrics

Metric Value
False Accept Rate (FAR) <0.01% (1 per 10,000 phrases)
False Reject Rate (FRR) <5% (misses 1 in 20)
Latency 80ms average
Power 7 mW (vs 300 mW for streaming)
ImportantWhy Not Just Use Raw Audio?

Memory explosion: - Raw audio (16-bit, 16 kHz): 32 KB/second - MFCCs (12 coefficients, 100 Hz): 4.8 KB/second - 6.7× reduction in data rate

Computational cost: - Raw audio neural network input: 400 samples per 25ms frame - MFCC neural network input: 12 coefficients per frame - 33× fewer input features → 100× faster inference

Noise robustness: - Raw audio captures all frequencies (including noise) - MFCCs focus on perceptually-relevant frequencies (300-8000 Hz)

Speaker independence: - Raw audio contains speaker pitch, timbre, accent - MFCCs capture phoneme content, not speaker identity

1345.6 Optimizing MFCC for Edge Devices

TipPractical Implementation Tips

Reduce computation: 1. Use 20 filters instead of 40 (still effective for keywords) 2. Pre-compute filterbank weights as INT16 3. Use ARM CMSIS-DSP library for optimized FFT 4. Circular buffer for overlapping frames (saves RAM)

Common Pitfall: Training in quiet office → fails in noisy homes

Solution: Add noise augmentation (MUSAN dataset, AudioSet) - Target SNR range: -5 dB to +20 dB

Testing Checklist: - [ ] Different microphones (phone, speaker, wearable) - [ ] Various distances (0.5m, 2m, 5m) - [ ] Background noise (TV, music, crowd) - [ ] Diverse speakers (age, gender, accent) - [ ] Measure real power consumption

1345.7 Complete IoT ML Lifecycle

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flowchart TB
    subgraph Collection["1. Data Collection"]
        Sensors[IoT Sensors<br/>Accelerometer, Audio]
        Raw[Raw Data<br/>High-rate sampling]
        Store[Time-Series DB]
    end

    subgraph Preparation["2. Data Preparation"]
        Clean[Data Cleaning]
        Window[Windowing]
        Features[Feature Extraction<br/>MFCC, Statistics]
    end

    subgraph Training["3. Model Training (Cloud)"]
        Split[Train/Val/Test Split]
        Train[Train Model]
        Eval[Evaluate]
    end

    subgraph Optimization["4. Model Optimization"]
        Quant[Quantization<br/>FP32 to INT8]
        Prune[Pruning]
        Compress[Compression]
    end

    subgraph Deployment["5. Edge Deployment"]
        Deploy[Deploy to Devices]
        Inference[Real-time Inference]
        Action[Take Action]
    end

    subgraph Monitor["6. Monitoring"]
        Track[Track Metrics]
        Drift[Detect Drift]
        Retrain[Retrain Pipeline]
    end

    Sensors --> Raw --> Store
    Store --> Clean --> Window --> Features
    Features --> Split --> Train --> Eval
    Eval --> Quant --> Prune --> Compress
    Compress --> Deploy --> Inference --> Action
    Action --> Track --> Drift --> Retrain
    Retrain --> Split

    style Sensors fill:#2C3E50,stroke:#16A085,color:#fff
    style Features fill:#16A085,stroke:#2C3E50,color:#fff
    style Train fill:#E67E22,stroke:#2C3E50,color:#fff
    style Compress fill:#27AE60,stroke:#2C3E50,color:#fff
    style Inference fill:#2C3E50,stroke:#16A085,color:#fff
    style Drift fill:#E74C3C,stroke:#2C3E50,color:#fff

Figure 1345.4: Complete IoT ML Lifecycle with Six Stages

1345.8 Knowledge Check

Question 1: Why is MFCC preferred over raw audio waveforms for keyword spotting?

Explanation: MFCC mimics human auditory perception with the Mel scale and compresses 16,000 samples per second to 13 coefficients per frame—99.9% compression! Neural networks learn these patterns effectively, achieving 95%+ accuracy with small models.

1345.9 Summary

This chapter covered audio feature extraction for IoT:

  • MFCC Pipeline: Pre-emphasis → Windowing → FFT → Mel filterbank → Log → DCT
  • Dimensionality Reduction: 6.7× data reduction enables edge processing
  • Wake Word Detection: Edge MFCC + tiny NN achieves <100ms latency, 7mW power
  • Hybrid Architecture: Edge for wake word, cloud for full ASR
  • Optimization: 20 filters, INT16 weights, CMSIS-DSP for efficiency

Key Insight: MFCCs encode domain knowledge (human hearing) into features, enabling small models to achieve high accuracy on resource-constrained devices.

1345.10 What’s Next

Continue to Feature Engineering to learn systematic approaches for designing discriminative features for any IoT sensor type.