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AI-Powered Voice Separation Algorithms: Testing Accuracy in Reconstructing Fundamental Frequency for Vocal Analysis.

Tiago Lima Bicalho Cruz1, Pedro Amarante Andrade2, Manuel Brandner3

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Summary
This summary is machine-generated.

Voice separation AI methods enable singing voice analysis from commercial recordings. Music.ai shows robust performance, improving feasibility of large-scale vocal studies.

Keywords:
Artificial intelligenceCommercial recordingsFundamental frequencySignal-to-noise ratioSinging voiceVoice separation

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Area of Science:

  • Music Information Retrieval
  • Signal Processing
  • Acoustic Analysis

Background:

  • Singing voice analysis typically requires high-quality recordings, limiting research on commercial recordings (CRs).
  • CRs present challenges like complex accompaniment, recording artifacts, and post-production, hindering accurate fundamental frequency (fo) extraction.
  • Existing voice separation methods need evaluation for their efficacy in analyzing fo from imperfect CRs.

Purpose of the Study:

  • To evaluate the effectiveness of voice separation techniques for reliable fundamental frequency (fo) extraction from commercial recordings.
  • To compare the performance of different AI-based separation methods against baselines under varying signal-to-noise ratios (SNRs).
  • To determine the feasibility of using these methods for large-scale analysis of singing voice characteristics in archival and commercial audio.

Main Methods:

  • Synthesized vocals with ground-truth fo were mixed with instrumental introductions from seven commercial recordings at five SNRs (-12 to +12 dB).
  • Applied methods included unfiltered baseline, bandpass filtering (2.2-6 kHz), iZotope RX10, Music.ai, and robust principal component analysis (RPCA).
  • Extracted fo contours using Praat and compared them to ground truth via success rate, resolved rate (≤10 cents deviation), and receiver operating characteristic analysis.

Main Results:

  • Music.ai demonstrated the most robust performance, achieving over 80% success at SNR ≥ 0 dB and degrading least at lower SNRs.
  • iZotope RX10 performed similarly at positive SNRs but showed greater decline with increased noise.
  • Bandpass filtering was comparable to top separation methods at higher SNRs; RPCA showed lower overall accuracy. Accuracy decreased below 0 dB and was influenced by accompaniment complexity and recording quality.

Conclusions:

  • AI-based voice separation methods, particularly Music.ai, can significantly enhance the analysis of singing voices in commercial and archival recordings.
  • These advancements make large-scale studies of vocal style and technique more feasible.
  • Continued validation across diverse genres and recording conditions is crucial to ensure reliable insights into vocal expression and performance nuances.