  This paper presents NOMAD (Non-Matching Audio Distance), a differentiable
perceptual similarity metric that measures the distance of a degraded signal
against non-matching references. The proposed method is based on learning deep
feature embeddings via a triplet loss guided by the Neurogram Similarity Index
Measure (NSIM) to capture degradation intensity. During inference, the
similarity score between any two audio samples is computed through Euclidean
distance of their embeddings. NOMAD is fully unsupervised and can be used in
general perceptual audio tasks for audio analysis e.g. quality assessment and
generative tasks such as speech enhancement and speech synthesis. The proposed
method is evaluated with 3 tasks. Ranking degradation intensity, predicting
speech quality, and as a loss function for speech enhancement. Results indicate
NOMAD outperforms other non-matching reference approaches in both ranking
degradation intensity and quality assessment, exhibiting competitive
performance with full-reference audio metrics. NOMAD demonstrates a promising
technique that mimics human capabilities in assessing audio quality with
non-matching references to learn perceptual embeddings without the need for
human-generated labels.
