  Target sound extraction (TSE) separates the target sound from the mixture
signals based on provided clues. However, the performance of existing models
significantly degrades under reverberant conditions. Inspired by auditory scene
analysis (ASA), this work proposes a TSE model provided with pitch information
named TSE-PI. Conditional pitch extraction is achieved through the Feature-wise
Linearly Modulated layer with the sound-class label. A modified Waveformer
model combined with pitch information, employing a learnable Gammatone
filterbank in place of the convolutional encoder, is used for target sound
extraction. The inclusion of pitch information is aimed at improving the
model's performance. The experimental results on the FSD50K dataset illustrate
2.4 dB improvements of target sound extraction under reverberant environments
when incorporating pitch information and Gammatone filterbank.
