Audio watermarking is a promising technology for copyright protection, yet traditional methods are limited that must be combined with auxiliary techniques against attacks. This article proposes a new audio watermarking method that embeds watermarks through a trained neural network. It adds small imperceptible perturbations to the original audio so that its deep features point to specific watermark features. Data augmentation and error correcting coding are employed to guarantee its practicable robustness. This method is robust against many attacks without auxiliary techniques and shows better performance than other deep learning-based methods.