Developed a comprehensive audio signal processing pipeline in Python that captures, analyzes, filters, and enhances recorded audio using real-time techniques.
- Recorded mono audio at 44.1 kHz using the
sounddevicelibrary. - Performed time-domain, frequency-domain, and spectrographic analysis for full signal insight.
- Applied a bandpass Butterworth filter (300 Hz–4000 Hz) to suppress low and high-frequency noise while preserving speech components.
- Built an amplification module with normalization to avoid clipping.
- Detected dominant frequency components and performed targeted noise band analysis (e.g., 50–300 Hz) for adaptive profiling.
- Exported audio to
.wavand enabled in-notebook playback withIPython.display.Audio.
Spectrograms show how frequency content varies over time — a critical feature for detecting and understanding non-stationary noise patterns that are not visible in static plots. This allows:
- Isolation of specific noise bands (e.g., hum, hiss, or chatter).
- Visual validation of filter effectiveness.
- Design of time-sensitive or adaptive filters.
Spectrographic insight is essential for tuning filters and verifying the success of noise cancellation techniques in dynamic environments.
- NumPy – Numerical computations
- SciPy – Signal processing (
fft,butter,filtfilt) and I/O (wavfile.write) - Matplotlib – Plotting waveform, frequency spectrum, and spectrograms
- SoundDevice – Real-time audio recording
- IPython.display – Audio playback inside Jupyter notebooks