Audio Analyzer

A comprehensive toolkit for analyzing audio files. Extract detailed information about audio including tempo, musical key, frequency content, loudness metrics, and generate professional visualizations.

Quick Start

python

from scripts.audio_analyzer import AudioAnalyzer

# Analyze an audio file
analyzer = AudioAnalyzer("song.mp3")
analyzer.analyze()

# Get all analysis results
results = analyzer.get_results()
print(f"BPM: {results['tempo']['bpm']}")
print(f"Key: {results['key']['key']} {results['key']['mode']}")

# Generate visualizations
analyzer.plot_waveform("waveform.png")
analyzer.plot_spectrogram("spectrogram.png")

# Full report
analyzer.save_report("analysis_report.json")

Features

•Tempo/BPM Detection: Accurate beat tracking with confidence score
•Key Detection: Musical key and mode (major/minor) identification
•Frequency Analysis: Spectrum, dominant frequencies, frequency bands
•Loudness Metrics: RMS, peak, LUFS, dynamic range
•Waveform Visualization: Multi-channel waveform plots
•Spectrogram: Time-frequency visualization with customization
•Chromagram: Pitch class visualization for harmonic analysis
•Beat Grid: Visual beat markers overlaid on waveform
•Export Formats: JSON report, PNG/SVG visualizations

API Reference

Initialization

python

# From file
analyzer = AudioAnalyzer("audio.mp3")

# With custom sample rate
analyzer = AudioAnalyzer("audio.wav", sr=44100)

Analysis Methods

python

# Run full analysis
analyzer.analyze()

# Individual analyses
analyzer.analyze_tempo()      # BPM and beat positions
analyzer.analyze_key()        # Musical key detection
analyzer.analyze_loudness()   # RMS, peak, LUFS
analyzer.analyze_frequency()  # Spectrum analysis
analyzer.analyze_dynamics()   # Dynamic range

Results Access

python

# Get all results as dict
results = analyzer.get_results()

# Individual results
tempo = analyzer.get_tempo()        # {'bpm': 120, 'confidence': 0.85, 'beats': [...]}
key = analyzer.get_key()            # {'key': 'C', 'mode': 'major', 'confidence': 0.72}
loudness = analyzer.get_loudness()  # {'rms_db': -14.2, 'peak_db': -0.5, 'lufs': -14.0}
freq = analyzer.get_frequency()     # {'dominant_freq': 440, 'spectrum': [...]}

Visualization Methods

python

# Waveform
analyzer.plot_waveform(
    output="waveform.png",
    figsize=(12, 4),
    color="#1f77b4",
    show_rms=True
)

# Spectrogram
analyzer.plot_spectrogram(
    output="spectrogram.png",
    figsize=(12, 6),
    cmap="magma",           # viridis, plasma, inferno, magma
    freq_scale="log",       # linear, log, mel
    max_freq=8000           # Hz
)

# Chromagram (pitch classes)
analyzer.plot_chromagram(
    output="chromagram.png",
    figsize=(12, 4)
)

# Onset strength / beat grid
analyzer.plot_beats(
    output="beats.png",
    figsize=(12, 4),
    show_strength=True
)

# Combined dashboard
analyzer.plot_dashboard(
    output="dashboard.png",
    figsize=(14, 10)
)

Export

python

# JSON report with all analysis
analyzer.save_report("report.json")

# Summary text
summary = analyzer.get_summary()
print(summary)

Analysis Details

Tempo Detection

Uses beat tracking algorithm to detect:

•BPM: Beats per minute (tempo)
•Beat positions: Timestamps of detected beats
•Confidence: Reliability score (0-1)

python

tempo = analyzer.get_tempo()
# {
#     'bpm': 128.0,
#     'confidence': 0.89,
#     'beats': [0.0, 0.469, 0.938, 1.406, ...],  # seconds
#     'beat_count': 256
# }

Key Detection

Analyzes harmonic content to identify:

•Key: Root note (C, C#, D, etc.)
•Mode: Major or minor
•Confidence: Detection confidence
•Key profile: Correlation with each key

python

key = analyzer.get_key()
# {
#     'key': 'A',
#     'mode': 'minor',
#     'confidence': 0.76,
#     'profile': {'C': 0.12, 'C#': 0.08, ...}
# }

Loudness Metrics

Comprehensive loudness analysis:

•RMS dB: Root mean square level
•Peak dB: Maximum sample level
•LUFS: Integrated loudness (broadcast standard)
•Dynamic Range: Difference between loud and quiet sections

python

loudness = analyzer.get_loudness()
# {
#     'rms_db': -14.2,
#     'peak_db': -0.3,
#     'lufs': -14.0,
#     'dynamic_range_db': 12.5,
#     'crest_factor': 8.2
# }

Frequency Analysis

Spectrum analysis including:

•Dominant frequency: Strongest frequency component
•Frequency bands: Energy in bass, mid, treble
•Spectral centroid: "Brightness" of audio
•Spectral rolloff: Frequency below which 85% of energy exists

python

freq = analyzer.get_frequency()
# {
#     'dominant_freq': 440.0,
#     'spectral_centroid': 2150.3,
#     'spectral_rolloff': 4200.5,
#     'bands': {
#         'sub_bass': -28.5,      # 20-60 Hz
#         'bass': -18.2,          # 60-250 Hz
#         'low_mid': -12.1,       # 250-500 Hz
#         'mid': -10.8,           # 500-2000 Hz
#         'high_mid': -14.3,      # 2000-4000 Hz
#         'high': -22.1           # 4000-20000 Hz
#     }
# }

CLI Usage

bash

# Full analysis with all visualizations
python audio_analyzer.py --input song.mp3 --output-dir ./analysis/

# Just tempo and key
python audio_analyzer.py --input song.mp3 --analyze tempo key --output report.json

# Generate specific visualization
python audio_analyzer.py --input song.mp3 --plot spectrogram --output spec.png

# Dashboard view
python audio_analyzer.py --input song.mp3 --dashboard --output dashboard.png

# Batch analyze directory
python audio_analyzer.py --input-dir ./songs/ --output-dir ./reports/

CLI Arguments

Argument	Description	Default
`--input`	Input audio file	Required
`--input-dir`	Directory of audio files	-
`--output`	Output file path	-
`--output-dir`	Output directory	`.`
`--analyze`	Analysis types: tempo, key, loudness, frequency, all	`all`
`--plot`	Plot type: waveform, spectrogram, chromagram, beats, dashboard	-
`--format`	Output format: json, txt	`json`
`--sr`	Sample rate for analysis	`22050`

Examples

Song Analysis

python

analyzer = AudioAnalyzer("track.mp3")
analyzer.analyze()

print(f"Tempo: {analyzer.get_tempo()['bpm']:.1f} BPM")
print(f"Key: {analyzer.get_key()['key']} {analyzer.get_key()['mode']}")
print(f"Loudness: {analyzer.get_loudness()['lufs']:.1f} LUFS")

analyzer.plot_dashboard("track_analysis.png")

Podcast Quality Check

python

analyzer = AudioAnalyzer("podcast.mp3")
analyzer.analyze_loudness()

loudness = analyzer.get_loudness()
if loudness['lufs'] > -16:
    print("Warning: Audio may be too loud for podcast standards")
elif loudness['lufs'] < -20:
    print("Warning: Audio may be too quiet")
else:
    print("Loudness is within podcast standards (-16 to -20 LUFS)")

Batch Analysis

python

import os
from scripts.audio_analyzer import AudioAnalyzer

results = []
for filename in os.listdir("./songs"):
    if filename.endswith(('.mp3', '.wav', '.flac')):
        analyzer = AudioAnalyzer(f"./songs/{filename}")
        analyzer.analyze()
        results.append({
            'file': filename,
            'bpm': analyzer.get_tempo()['bpm'],
            'key': f"{analyzer.get_key()['key']} {analyzer.get_key()['mode']}",
            'lufs': analyzer.get_loudness()['lufs']
        })

# Sort by BPM for DJ set
results.sort(key=lambda x: x['bpm'])

Supported Formats

Input formats (via librosa/soundfile):

•MP3
•WAV
•FLAC
•OGG
•M4A/AAC
•AIFF

Output formats:

•JSON (analysis report)
•PNG (visualizations)
•SVG (visualizations)
•TXT (summary)

Dependencies

code

librosa>=0.10.0
soundfile>=0.12.0
matplotlib>=3.7.0
numpy>=1.24.0
scipy>=1.10.0

Limitations

•Key detection works best with melodic content (less accurate for drums/percussion)
•BPM detection may struggle with free-tempo or complex time signatures
•Very short clips (<5 seconds) may have reduced accuracy
•LUFS calculation is simplified (not full ITU-R BS.1770-4)