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Transformer architecture has enabled recent progress in speech enhancement.Since Transformers are position-agostic, positional encoding is the de factostandard component used to enable Transformers to distinguish the order ofelements in a sequence. However, it remains unclear how positional encodingexactly impacts speech enhancement based on Transformer architectures. In thispaper, we perform a comprehensive empirical study evaluating five positionalencoding methods, i.e., Sinusoidal and learned absolute position embedding(APE), T5-RPE, KERPLE, as well as the Transformer without positional encoding(No-Pos), across both causal and noncausal configurations. We conduct extensivespeech enhancement experiments, involving spectral mapping and masking methods.Our findings establish that positional encoding is not quite helpful for themodels in a causal configuration, which indicates that causal attention mayimplicitly incorporate position information. In a noncausal configuration, themodels significantly benefit from the use of positional encoding. In addition,we find that among the four position embeddings, relative position embeddingsoutperform APEs.

An Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement