Paper year
2026
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Siamese network contrastive learning model for the objective evaluation of singing sound quality
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Abstract
This study extracts target singers' voices from mixed audio with background music and noise, addressing the subjectivity, instability and lack of objective standards in traditional evaluation.An innovative SNN-SpEx+ method is proposed, combining Siamese neural network (SNN) and contrastive learning-based SpEx+.Its parameter-sharing twin architecture unifies the feature space for reference and mixed speech, breaking the bottleneck of feature space dislocation in traditional dual-network structures.Contrastive learning is integrated into vocal extraction to build a 'separation is learning' joint optimisation framework, enhancing adaptability to unknown singers and short reference voices.Experiments on MUSDB18-HQ and NSynth-Singer show SNN-SpEx+ outperforms SpEx++ by 0.85 dB in SI-SDRi and 0.17 in PESQ.For short references (<2 s), its SI-SDRi drops only 3.16 dB (3.4 dB lower than SpEx++), providing an automatic standardised evaluation tool for music education and singer selection with broad prospects.
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