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Updated: Jun 23, 2025

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Impact of Audio Data Compression on Feature Extraction for Vocal Biomarker Detection: Validation Study.

Jessica Oreskovic1, Jaycee Kaufman1, Yan Fossat1

  • 1Klick Labs, Toronto, ON, Canada.

JMIR Biomedical Engineering
|June 14, 2024
PubMed
Summary
This summary is machine-generated.

Audio data compression impacts vocal biomarkers, with some features remaining stable across formats. MediaHuman (MH) and FFmpeg converters showed greater resilience, crucial for healthcare applications using compressed voice data.

Keywords:
Pythonacousticacousticsalgorithmalgorithmsaudioaudio compressionbiomarkerbiomarkerscompressiondetectdetectionfeature extractionsoundsoundsspeechvocal biomarkervoice

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Area of Science:

  • Biomedical Engineering
  • Acoustic Analysis
  • Digital Signal Processing

Background:

  • Vocal biomarkers offer noninvasive medical screening and diagnostics.
  • Previous studies showed feasibility of predicting type 2 diabetes mellitus from speech.
  • This study investigates audio compression's impact on vocal biomarker development.

Purpose of the Study:

  • To analyze how MP3, M4A, and WMA compression affect vocal biomarker features.
  • To evaluate the influence of 3 conversion tools and 2 bitrates on feature detection.
  • To determine the impact of audio compression on acoustic vocal biomarker development.

Main Methods:

  • Compared uncompressed voice samples converted to MP3, M4A, WMA at 320 and 128 kbps.
  • Utilized MediaHuman (MH), WonderShare (WS), and FFmpeg conversion tools.
  • Extracted features like pitch, jitter, intensity, and Mel-frequency cepstral coefficients (MFCCs) from 17,298 smartphone recordings.
  • Applied Wilcoxon signed rank tests and Bonferroni correction for statistical analysis.

Main Results:

  • Compression significantly impacted various voice features and MFCCs.
  • MediaHuman (MH) converter showed greater resilience than WonderShare (WS).
  • Voice features demonstrated greater stability than Mel-frequency cepstral coefficients (MFCCs) across conversion methods.
  • Compression effects were feature-specific, with some features consistently altered.

Conclusions:

  • Audio compression effects on vocal biomarkers are feature-specific.
  • MediaHuman (MH) and FFmpeg converters are more resilient to compression.
  • Understanding feature stability is vital for diagnostic applications using compressed voice data.
  • Findings support the use of stable vocal features in healthcare applications with compressed audio data.