<p>Anomalous Sound Detection (ASD) plays a critical role in practical scenarios ranging from audio surveillance to industrial machine condition monitoring. A big challenge of current methods is their poor generalization ability in detecting unknown anomalous sounds. Existing dominant solutions primarily adopt Two-Dimensional Convolutional Neural Networks (2D-CNNs) to extract features from the time-frequency spectrograms of audio signals. Nevertheless, the inherent limited receptive field of 2D-CNNs fails to fully capture and utilize comprehensive time-frequency information from spectrograms, leading to insufficient feature representation and degraded detection performance. To this end, in this paper, a novel Dual-path Channel-attention WaveNet (DC-WaveNet) is proposed to learn discriminative, robust audio feature representations for ASD tasks. The core novelty of the proposed DC-WaveNet lies in its dual-path feature learning paradigm equipped with channel-attention WaveNet encoders. Specifically, the network first excavates fine-grained latent feature information from the frequency dimension of audio signals, and further aggregates and models temporal dimension output features to capture global overall audio characteristics, ultimately generating highly discriminative feature representations suitable for anomalous sound identification.</p>