SpectNet：使用可学习频谱图的端到端音频信号分类
SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms
摘要：
从音频信号中进行模式识别是一个活跃的研究课题，包括音频标记、声学场景分类、音乐分类和其他领域。频谱图和梅尔倒谱系数（MFCC）是音频信号分析和分类中最常用的特征之一。最近，深度卷积神经网络（CNN）已成功用于使用基于频谱图的2D特征的音频分类问题。在本文中，我们提出了SpectNet，这是一个集成的前端层，可以在CNN架构中提取频谱图特征，可用于音频模式识别任务。前端层利用可学习的gammatone滤波器，这些滤波器使用梅尔尺度滤波器进行初始化。所提出的层输出2D频谱图图像，其可以被馈送到2D CNN中用于分类。整个网络的参数，包括前端滤波器组，可以通过反向传播进行更新。该训练方案允许根据目标音频数据集微调频谱图图像特征。所提出的方法进行评估，在两个不同的音频信号分类任务：心音异常检测和声学场景分类。与经典的谱图图像特征相比，该方法在心音分类任务中的MACC提高了1.02%，在声学场景分类任务中的准确率提高了2.11%.我们实验的源代码可以在\url{https://github.com/mHealthBuet/SpectNet}上找到
英文摘要：
Pattern recognition from audio signals is an active research topic encompassing audio tagging, acoustic scene classification, music classification, and other areas. Spectrogram and mel-frequency cepstral coefficients (MFCC) are among the most commonly used features for audio signal analysis and classification. Recently, deep convolutional neural networks (CNN) have been successfully used for audio classification problems using spectrogram-based 2D features. In this paper, we present SpectNet, an integrated front-end layer that extracts spectrogram features within a CNN architecture that can be used for audio pattern recognition tasks. The front-end layer utilizes learnable gammatone filters that are initialized using mel-scale filters. The proposed layer outputs a 2D spectrogram image which can be fed into a 2D CNN for classification. The parameters of the entire network, including the front-end filterbank, can be updated via back-propagation. This training scheme allows for fine-tuning the spectrogram-image features according to the target audio dataset. The proposed method is evaluated in two different audio signal classification tasks: heart sound anomaly detection and acoustic scene classification. The proposed method shows a significant 1.02\% improvement in MACC for the heart sound classification task and 2.11\% improvement in accuracy for the acoustic scene classification task compared to the classical spectrogram image features. The source code of our experiments can be found at \url{https://github.com/mHealthBuet/SpectNet}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
