Initial release v1.0.0
This commit is contained in:
Kevin O'Neill
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# Audio Analysis: Standard Mode (Heuristic Approach)
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## Overview
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The Lidify audio analyzer has two modes:
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- **Enhanced Mode**: Uses TensorFlow ML models for accurate mood/valence/arousal predictions
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- **Standard Mode**: Uses signal processing heuristics when ML models aren't available
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This document covers the **Standard Mode** implementation for code review.
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---
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Docker Container │
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│ lidify_audio_analyzer │
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│ │
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│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
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│ │ Redis │◄───│ Worker │───►│ PostgreSQL │ │
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│ │ Job Queue │ │ Loop │ │ Track Table │ │
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│ └─────────────┘ └──────┬──────┘ └─────────────────────┘ │
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│ │ │
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│ ┌──────▼──────┐ │
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│ │ AudioAnalyzer│ │
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│ │ Class │ │
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│ └──────┬──────┘ │
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│ │ │
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│ ┌────────────────┼────────────────┐ │
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│ ▼ ▼ ▼ │
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│ ┌───────────────┐ ┌─────────────┐ ┌──────────────────┐ │
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│ │ Basic Features│ │ Spectral │ │ Heuristic │ │
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│ │ (BPM, Key) │ │ Analysis │ │ Mood Estimation │ │
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│ └───────────────┘ └─────────────┘ └──────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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---
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## File Structure
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```
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services/audio-analyzer/
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├── analyzer.py # Main analyzer code (870 lines)
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├── requirements.txt # Python dependencies
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└── Dockerfile # Container build configuration
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```
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---
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## Key Classes
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### 1. `AudioAnalyzer` (Line 130-660)
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Main analysis class with two modes:
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```python
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class AudioAnalyzer:
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def __init__(self):
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self.enhanced_mode = False # Falls back to Standard if ML unavailable
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self._init_essentia() # Initialize signal processing algorithms
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self._load_ml_models() # Attempt to load ML models
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```
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### 2. `AnalysisWorker` (Line 663-847)
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Redis queue worker that:
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1. Polls for pending tracks from `audio:analysis:queue`
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2. Falls back to scanning `Track` table for `analysisStatus = 'pending'`
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3. Processes tracks and updates database
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---
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## Standard Mode: Heuristic Calculations
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### Input Features (Always Extracted)
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| Feature | Essentia Algorithm | Description |
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|---------|-------------------|-------------|
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| BPM | `RhythmExtractor2013` | Beats per minute |
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| Key/Scale | `KeyExtractor` | Musical key (C, D#, etc.) and mode (major/minor) |
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| Loudness | `Loudness` | Perceived loudness in dB |
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| Dynamic Range | `DynamicComplexity` | Difference between quiet and loud parts |
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| Danceability | `Danceability` | How suitable for dancing (0-1) |
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| RMS Energy | `RMS` | Root Mean Square amplitude per frame |
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| Spectral Centroid | `Centroid` | "Brightness" - center of spectral mass |
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| Spectral Flatness | `FlatnessDB` | Noise-like vs tonal content |
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| Zero-Crossing Rate | `ZeroCrossingRate` | Rate of signal sign changes |
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### Frame-Based Processing (Lines 328-365)
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```python
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frame_size = 2048
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hop_size = 1024
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for i in range(0, len(audio_44k) - frame_size, hop_size):
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frame = audio_44k[i:i + frame_size]
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windowed = self.windowing(frame)
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spectrum = self.spectrum(windowed)
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rms_values.append(self.rms(frame))
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zcr_values.append(self.zcr(frame))
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spectral_centroid_values.append(self.spectral_centroid(spectrum))
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spectral_flatness_values.append(self.spectral_flatness(spectrum))
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```
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---
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## Heuristic Formulas
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### Energy (Line 347-353)
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**Problem Solved**: Previous implementation used `es.Energy()` which returns sum of squared samples (huge number), normalized incorrectly as `energy / 100`.
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**Current Implementation**:
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```python
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avg_rms = np.mean(rms_values)
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energy = min(1.0, avg_rms * 3) # RMS typically 0.0-0.5, scale to 0-1
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```
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---
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### Valence (Happiness/Positivity) - Lines 495-518
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**Formula**:
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```
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valence = key_valence * 0.40
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+ bpm_valence * 0.25
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+ brightness_valence * 0.20
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+ energy * 0.15
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```
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**Components**:
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| Component | Weight | Calculation | Rationale |
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|-----------|--------|-------------|-----------|
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| Key Valence | 40% | Major = 0.65, Minor = 0.35 | Major keys sound happier |
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| BPM Valence | 25% | Fast (≥120) → 0.8, Slow (≤80) → 0.2 | Fast tempo = upbeat |
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| Brightness | 20% | `spectral_centroid * 1.5` | Bright sounds feel positive |
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| Energy | 15% | RMS energy (0-1) | Loud = energetic/positive |
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**Code**:
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```python
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# Key contribution
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key_valence = 0.65 if scale == 'major' else 0.35
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# BPM contribution
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if bpm >= 120:
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bpm_valence = min(0.8, 0.5 + (bpm - 120) / 200)
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elif bpm <= 80:
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bpm_valence = max(0.2, 0.5 - (80 - bpm) / 100)
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else:
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bpm_valence = 0.5
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# Brightness contribution
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brightness_valence = min(1.0, spectral_centroid * 1.5)
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# Final weighted sum
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result['valence'] = round(
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key_valence * 0.4 +
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bpm_valence * 0.25 +
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brightness_valence * 0.2 +
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energy * 0.15,
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3
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)
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```
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---
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### Arousal (Energy/Intensity) - Lines 520-543
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**Formula**:
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```
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arousal = bpm_arousal * 0.35
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+ energy_arousal * 0.35
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+ brightness_arousal * 0.15
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+ compression_arousal * 0.15
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```
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**Components**:
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| Component | Weight | Calculation | Rationale |
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|-----------|--------|-------------|-----------|
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| BPM Arousal | 35% | `(bpm - 60) / 140` mapped to 0.1-0.9 | Fast = high energy |
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| Energy | 35% | RMS energy (0-1) | Loud = intense |
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| Brightness | 15% | `spectral_centroid * 1.2` | Bright = energetic |
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| Compression | 15% | `1 - (dynamic_range / 20)` | Compressed = intense/modern |
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**Code**:
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```python
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# BPM contribution (60-180 BPM → 0.1-0.9)
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bpm_arousal = min(0.9, max(0.1, (bpm - 60) / 140))
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# Energy is direct intensity indicator
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energy_arousal = energy
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# Low dynamic range = compressed = more intense
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compression_arousal = max(0, min(1.0, 1 - (dynamic_range / 20)))
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# Brightness adds perceived energy
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brightness_arousal = min(1.0, spectral_centroid * 1.2)
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result['arousal'] = round(
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bpm_arousal * 0.35 +
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energy_arousal * 0.35 +
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brightness_arousal * 0.15 +
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compression_arousal * 0.15,
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3
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)
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```
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---
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### Instrumentalness - Lines 545-563
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**Approach**: Estimate likelihood of vocals vs instrumental based on spectral characteristics.
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**Formula**:
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```
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instrumentalness = flatness_normalized * 0.6 + zcr_instrumental * 0.4
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```
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**Components**:
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| Component | Weight | Calculation | Rationale |
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|-----------|--------|-------------|-----------|
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| Spectral Flatness | 60% | `(flatness + 40) / 40` | Noise-like (0dB) = instrumental; Tonal (-60dB) = vocals |
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| ZCR Pattern | 40% | Low (<0.05) = 0.7; High (>0.15) = 0.4 | Sustained tones = instrumental |
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**Code**:
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```python
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# Spectral flatness: -40dB to 0dB → 0 to 1
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flatness_normalized = min(1.0, max(0, (spectral_flatness + 40) / 40))
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# ZCR patterns
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if zcr < 0.05:
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zcr_instrumental = 0.7 # Sustained instrumental tones
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elif zcr > 0.15:
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zcr_instrumental = 0.4 # Could be speech or percussion
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else:
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zcr_instrumental = 0.5 # Uncertain
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result['instrumentalness'] = round(
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flatness_normalized * 0.6 + zcr_instrumental * 0.4,
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3
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)
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```
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---
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### Acousticness - Line 565-568
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**Simple heuristic**: High dynamic range suggests acoustic recording (natural dynamics preserved).
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```python
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result['acousticness'] = round(min(1.0, dynamic_range / 12), 3)
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```
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| Dynamic Range | Acousticness | Interpretation |
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|---------------|--------------|----------------|
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| < 6 dB | < 0.5 | Heavily compressed (electronic/pop) |
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| 6-12 dB | 0.5-1.0 | Moderate (mixed) |
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| > 12 dB | 1.0 | High dynamic range (acoustic/classical) |
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---
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### Speechiness - Lines 570-575
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**Approach**: Speech has characteristic ZCR + spectral centroid patterns.
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```python
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if zcr > 0.08 and zcr < 0.2 and spectral_centroid > 0.1 and spectral_centroid < 0.4:
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result['speechiness'] = round(min(0.5, zcr * 3), 3)
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else:
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result['speechiness'] = 0.1
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```
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| Condition | Result |
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|-----------|--------|
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| ZCR 0.08-0.2 AND centroid 0.1-0.4 | Speech-like (up to 0.5) |
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| Outside range | Low speechiness (0.1) |
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---
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## Mood Tag Generation (Lines 581-660)
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Tags are derived from computed features:
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| Condition | Tags Added |
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|-----------|------------|
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| `arousal >= 0.7` | energetic, upbeat |
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| `arousal <= 0.3` | calm, peaceful |
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| `valence >= 0.7` | happy, uplifting |
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| `valence <= 0.3` | sad, melancholic |
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| `danceability >= 0.7` | dance, groovy |
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| `bpm >= 140` | fast |
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| `bpm <= 80` | slow |
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| `keyScale == 'minor'` (and not happy) | moody |
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| `arousal >= 0.7 AND bpm >= 120` | workout |
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| `arousal <= 0.4 AND valence <= 0.4` | atmospheric |
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| `arousal <= 0.3 AND bpm <= 90` | chill |
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---
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## Output Schema
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```typescript
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interface AnalysisResult {
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// Basic features
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bpm: number; // 60-200 typical
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beatsCount: number; // Total beat count
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key: string; // "C", "D#", etc.
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keyScale: string; // "major" or "minor"
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keyStrength: number; // 0-1 confidence
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// Energy metrics
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energy: number; // 0-1 (RMS-based)
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loudness: number; // dB
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dynamicRange: number; // dB
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// Heuristic estimates
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danceability: number; // 0-1
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valence: number; // 0-1 (happiness)
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arousal: number; // 0-1 (energy)
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instrumentalness: number; // 0-1
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acousticness: number; // 0-1
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speechiness: number; // 0-1
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// Derived
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moodTags: string[]; // ["calm", "peaceful", "chill"]
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analysisMode: "standard"; // Always "standard" for this mode
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}
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```
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---
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## Database Update (Lines 766-822)
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All features are persisted to the `Track` table:
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```sql
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UPDATE "Track"
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SET
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bpm = %s,
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"beatsCount" = %s,
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key = %s,
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"keyScale" = %s,
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"keyStrength" = %s,
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energy = %s,
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loudness = %s,
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"dynamicRange" = %s,
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danceability = %s,
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valence = %s,
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arousal = %s,
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instrumentalness = %s,
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acousticness = %s,
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speechiness = %s,
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"moodTags" = %s,
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"analysisMode" = 'standard',
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"analysisStatus" = 'completed',
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"analysisVersion" = %s,
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"analyzedAt" = %s
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WHERE id = %s
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```
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---
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## Known Limitations
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### Standard Mode vs ML Models
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| Aspect | Standard Mode | Enhanced Mode (ML) |
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|--------|--------------|-------------------|
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| Valence accuracy | ~60% correlation | ~85% correlation |
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| Arousal accuracy | ~65% correlation | ~88% correlation |
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| Mood detection | Rule-based | Neural network |
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| Processing speed | Fast (~1-2 sec) | Slower (~5-10 sec) |
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| Dependencies | Essentia only | Essentia + TensorFlow |
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### Edge Cases
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1. **Ambient music**: Low BPM detection reliability
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2. **Classical**: Variable tempo causes BPM averaging issues
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3. **Spoken word**: May be misclassified as low-energy music
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4. **Electronic/EDM**: Compression detection may overestimate arousal
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---
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## Dependencies
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```
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# requirements.txt
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essentia==2.1b6.dev1110
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essentia-tensorflow==2.1b6.dev1110
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numpy>=1.21.0,<2.0.0
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tensorflow==2.15.0
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redis>=4.5.0
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psycopg2-binary>=2.9.0
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```
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---
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## Testing
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Run single file analysis:
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```bash
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docker exec lidify_audio_analyzer python3 analyzer.py --test /music/path/to/song.mp3
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```
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Example output:
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```json
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{
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"bpm": 128.5,
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"beatsCount": 256,
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"key": "C",
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"keyScale": "minor",
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"keyStrength": 0.723,
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"energy": 0.65,
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"loudness": -8.2,
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"dynamicRange": 7.5,
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"danceability": 0.72,
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"valence": 0.42,
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"arousal": 0.68,
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"instrumentalness": 0.35,
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"acousticness": 0.625,
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"speechiness": 0.1,
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"moodTags": ["energetic", "upbeat", "moody", "dance"],
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"analysisMode": "standard"
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}
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```
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---
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## Related Files
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||||
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- `services/audio-analyzer/Dockerfile` - Container build
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- `backend/src/services/vibeMatching.ts` - Uses these features for song matching
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- `prisma/schema.prisma` - Track table schema with analysis columns
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Reference in New Issue
Block a user