Audio Analyzer

Small, focused tools to record audio from your microphone and visualize it as a waveform, FFT spectrum, and spectrogram. Includes:

• A live, low-latency viewer built with PyQtGraph.
• A one-shot recorder that saves a WAV and an analysis image.

Files:

• live_audio_to_chart.py — real-time visualization (waveform, FFT, spectrogram).
• audio_to_charts.py — records 1 second and saves charts to an image.

Quick start

1. Create and activate a virtual environment (optional, recommended):

2. Install dependencies:

python -m pip install -r requirements.txt

3. Give your terminal/app microphone permission (OS-dependent).

How to run

Live viewer (recommended)

Shows waveform, FFT, and spectrogram in real-time. Use the docked spin box to change sampling rate.

python live_audio_to_chart.py

What you’ll see:

• Waveform (top)
  • Time range: last 1 second (modifiable).
  • Y range: [-0.05, 0.05] (audio from sounddevice is normalized to [-1, 1]) (modifiable).
• FFT Spectrum (middle)
  • Hann window applied to the 1-second buffer.
  • Frequency axis limited to 0–6 kHz (modifiable) for readability.
• Spectrogram (bottom)
  • STFT size: 1024 (Hann window) (modifiable).
  • Scrolls left to right using a fixed-width image buffer.
  • Colormap: inferno, levels in dB [-80, 0].

Controls:

• “Sampling rate (Hz)” spin box: 2000–96000 Hz. The app restarts the input stream when this changes.

Script: live_audio_to_chart.py

One-shot analysis (records 1 second)

Records 1 second at 44.1 kHz mono, writes a WAV, and saves charts (waveform, FFT, STFT spectrogram, Mel spectrogram) to an image.

python audio_to_charts.py

Outputs:

• recording.wav — 16-bit PCM.
• audio_analisis.png — figure with 4 panels:
  • Waveform
  • DFT (magnitude spectrum)
  • STFT Spectrogram (n_fft=1024, hop_length=512)
  • Mel Spectrogram (64 mels, HTK scale)

Script: audio_to_charts.py