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This paper presents NOMAD (Non-Matching Audio Distance), a differentiableperceptual similarity metric that measures the distance of a degraded signalagainst non-matching references. The proposed method is based on learning deepfeature embeddings via a triplet loss guided by the Neurogram Similarity IndexMeasure (NSIM) to capture degradation intensity. During inference, thesimilarity score between any two audio samples is computed through Euclideandistance of their embeddings. NOMAD is fully unsupervised and can be used ingeneral perceptual audio tasks for audio analysis e.g. quality assessment andgenerative tasks such as speech enhancement and speech synthesis. The proposedmethod is evaluated with 3 tasks. Ranking degradation intensity, predictingspeech quality, and as a loss function for speech enhancement. Results indicateNOMAD outperforms other non-matching reference approaches in both rankingdegradation intensity and quality assessment, exhibiting competitiveperformance with full-reference audio metrics. NOMAD demonstrates a promisingtechnique that mimics human capabilities in assessing audio quality withnon-matching references to learn perceptual embeddings without the need forhuman-generated labels.

NOMAD: Unsupervised Learning of Perceptual Embeddings for Speech Enhancement and Non-matching Reference Audio Quality Assessment