Background Noise Reduction In Audio-Recorded Interviews: Recording and Post-Processing Strategies
DOI:
https://doi.org/10.63556/ankad.v10i3.471Keywords:
interview, audio recording, background noise, noise reductio, digital signal processingAbstract
This study aims to develop technical and methodological solution recommendations for audio recording quality problems frequently encountered during the data collection process in qualitative research. Audio recordings used in interviews, especially in field settings, are affected by environmental noise, microphone placement, acoustic conditions, and speaker-specific variables, which reduce the reliability and analyzability of the data. In addition to appropriate microphone selection, basic acoustic arrangements, and filtering strategies for the pre- and post-recording processes, a hybrid audio enhancement system based on digital signal processing (DSP) and artificial intelligence (AI) developed in a Python environment is presented. Accordingly, the system consists of steps including energy- and spectral flatness-based adaptive thresholding, hum and click/plosive cleanup, fully adaptive spectral noise reduction, and speech foregrounding. Also, it includes a Demucs-based source separation module and an automatic Turkish transcription module implemented with the Whisper model. System performance was evaluated by comparing the model output with a reference transcription created by the researcher. In this validation process, Word Error Rate (WER) and Character Error Rate (CER) metrics were calculated, and normalization procedures for Turkish characters were applied. According to the sample validation results, the system achieved 79%–95% word accuracy and 88%–97% character accuracy; WER was 0.203, and CER was 0.114. The findings show that the developed hybrid structure provides a usable technical framework for qualitative research in terms of both audio enhancement and automatic transcription accuracy.
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