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Monoaural audio source separation is a challenging research area in machinelearning. In this area, a mixture containing multiple audio sources is given,and a model is expected to disentangle the mixture into isolated atomicsources. In this paper, we first introduce a challenging new dataset formonoaural source separation called WildMix. WildMix is designed with the goalof extending the boundaries of source separation beyond what previous datasetsin this area would allow. It contains diverse in-the-wild recordings from 25different sound classes, combined with each other using arbitrary compositionpolicies. Source separation often requires modeling long-range dependencies inboth temporal and spectral domains. To this end, we introduce a noveltrasnformer-based model called Spectro-Temporal Transformer (STT). STT utilizesa specialized encoder, called Spectro-Temporal Encoder (STE). STE highlightstemporal and spectral components of sources within a mixture, using aself-attention mechanism. It subsequently disentangles them in a hierarchicalmanner. In our experiments, STT swiftly outperforms various previous baselinesfor monoaural source separation on the challenging WildMix dataset.

WildMix Dataset and Spectro-Temporal Transformer Model for Monoaural Audio Source Separation