Room Acoustics and Microphone Characteristics Show Systematic Impact on Sound Event Recognition

The robustness of audio pattern recognition systems under varying acoustic conditions and hardware remains a critical challenge for real-world applications. We examine how room acoustics, microphone characteristics, and overlapping events affect classification performance for domestic events. We conducted experiments in four rooms at the University of Surrey-with reverberation times (RT60: 0.27-0.78 s, 50 Hz-10 kHz) and clarity indices (C50: 11.6-18.5 dB; C80: 13.1-25.9 dB, 500 Hz-1 kHz)-using four microphones (USB Condenser, ICS-43432 stereo, AudioMoth, and Earthworks M23 reference). For two CNN-14 architectures, baseline performance (obtained from the original audio) was used for comparison with different microphone/room configurations. Results (expressed as the percentage of audio frames correctly detected versus ground truth) show: First, high RT60 degraded detection of impulsive events (e.g., door knocks) by approximately 50%, while sustained events (e.g., speech, music) remained above 90%. Second, overlapping events produced masking effects that reduced performance by about 20%. Third, while microphone differences affect accuracy, low-cost devices matched reference performance for speech and music classes. Both architectures exhibited similar degradation patterns across conditions. These results underscore the need for improved acoustic characterization and hardware-aware processing. Future work should integrate adaptive feature extraction and training strategies to mitigate reverberation and overlap in complex environments.