Automated Audio Testing for FPS Games: Process, Tools, and Similarity Measurement

During game testing, various audio issues—missing jump/land sounds, incomplete sprint footsteps, continuous weapon noises, or inconsistent voice‑over levels—can severely affect player experience, yet they are often overlooked. The article explores an automated audio testing approach to quickly detect such problems.

Four typical audio bugs are highlighted: (1) APEX audio loss, (2) Overwatch loudness anomalies, (3) Borderlands audio errors, and (4) R6S audio disappearance. These examples illustrate the impact of missing or distorted sounds on gameplay.

The proposed workflow consists of three stages. First, an audio recording step is added to automated test scripts, using a virtual sound card (e.g., VBCable ) and command‑line recorder ( fmedia ) or Python Soundcard library to capture in‑game sounds. Second, recorded files are processed to extract audio features such as MFCC, short‑time energy, frequency, and time‑frequency analysis (often via librosa ). Third, similarity between a new recording and a baseline is measured using metrics like cosine distance, Euclidean distance, cross‑correlation, and difference functions, with thresholds (all scores >85 or a few low scores compensated by >95 values) to decide if the audio is acceptable.

Feature extraction details include:

MFCC (Mel‑frequency cepstral coefficients) for speech‑related characteristics.

Short‑time energy to gauge instantaneous loudness.

Frequency analysis for pitch and timbre.

Time‑frequency analysis to observe how spectral content evolves.

Similarity calculation can be performed with common scientific libraries such as sklearn or scipy , avoiding manual implementation of complex mathematics.

Practical tips suggest using a Conda + Jupyter environment, with VSCode extensions for easier debugging. The workflow is demonstrated with an AK‑47 gun‑shot test: the script triggers firing, records the audio, extracts features, computes similarity, and flags any deviation.

In conclusion, automated audio checks complement manual listening, helping QA quickly identify missing or mismatched sounds, improve overall game quality, and reduce human error, while still requiring occasional ear‑based verification.