Learning acoustic models directly from the raw waveform data with minimal
processing is challenging. Current waveform-based models have generally used
very few (~2) convolutional layers, which might be insufficient for building
high-level discriminative features. In this work, we propose very deep
convolutional neural networks (CNNs) that directly use time-domain waveforms as
inputs. Our CNNs, with up to 34 weight layers, are efficient to optimize over
very long sequences (e.g., vector of size 32000), necessary for processing
acoustic waveforms. This is achieved through batch normalization, residual
learning, and a careful design of down-sampling in the initial layers. Our
networks are fully convolutional, without the use of fully connected layers and
dropout, to maximize representation learning. We use a large receptive field in
the first convolutional layer to mimic bandpass filters, but very small
receptive fields subsequently to control the model capacity. We demonstrate the
performance gains with the deeper models. Our evaluation shows that the CNN
with 18 weight layers outperform the CNN with 3 weight layers by over 15% in
absolute accuracy for an environmental sound recognition task and matches the
performance of models using log-mel features.

本文提出了一种利用深度卷积神经网络从原始波形数据中直接学习音频模型的方法，通过批归一化、剩余学习和精心设计的下采样实现高效处理音频波形，并在环境声音识别任务中取得了 15% 的性能提升，达到了使用对数 - 梅尔特征的模型的性能。