agc-tuner

Pumping artifacts occur when AGC parameters are misconfigured. AGC tuning analyzes your signal's RMS and peak dynamics to recommend optimal attack_time, release_time, and target_level settings that eliminate breathing effects and stabilize audio output.

Optimal AGC parameters vary by signal type. AGC tuning measures signal characteristics and generates mode-specific parameter recommendations for FM, AM, SSB, and digital demodulation, accounting for each mode's unique dynamic range and response requirements.

AGC tuning performs side-by-side A/B comparisons by measuring RMS, peak levels, and crest factor across channels, visualizing gain changes over time, and updating settings via API or configuration files to identify which parameters work best for each channel.

Yes. AGC tuning diagnoses both quiet and loud audio by analyzing dynamic characteristics and recommending target_level adjustments alongside attack and release timing to achieve consistent, properly-leveled output for SDR listening scenarios.

You need access to a WaveCap-SDR channel, numpy and scipy for signal analysis, matplotlib for visualization, and the ability to connect via API or modify configuration files. AGC tuning requires live or recorded signal samples to measure dynamics accurately.

AGC tuning generates recommendations and performs comparisons based on your current channel setup, then applies updates via API or configuration file modifications, allowing you to test recommended parameters and refine settings for custom modes and channel-specific requirements.